Method for providing image and electronic device supporting the method

Abstract

According to various embodiments of the present invention, an electronic device includes a camera module, a processor functionally connected to the camera module, and a memory functionally connected to the processor, wherein the memory can store instructions that, when executed, cause the processor to: obtain a first image; identify a first object in the first image; obtain parameters associated with a zoom area based on movement of the first object in the first image; identify a first object corresponding to a second object included in a second image obtained by the camera module; and obtain an image of the second object from the second image based on the parameters associated with the zoom area.

Description

Technical Field

[0001] Various embodiments of the present disclosure relate to a method for providing an image and an electronic device supporting the method. Background Art

[0002] A user of an electronic device, such as a smartphone, captures an image using a camera and shares the captured image with other users. For example, the electronic device can track the movement of a subject (e.g., a person) (e.g., the movement of a dancing subject) by acquiring images using the camera. The user of the electronic device can repeatedly capture images to capture a desired image. When the user's desired image is captured, the electronic device can allow the user to share the captured image with other users and upload the captured image to a server providing an image sharing service.

[0003] The electronic device can acquire an image centered on an object corresponding to a subject to be captured. The electronic device can configure a zoom area to acquire an image in which the background other than the object is excluded as much as possible from the image to allow the object to be displayed at an appropriate size. The electronic device can acquire an image including the object by enlarging (zooming in) or reducing (zooming out) the zoom area. Summary of the Invention

[0004] Technical issues

[0005] Various embodiments of the present disclosure relate to a method for providing a video and an electronic device for supporting the method, which can enable a video related to an object to be acquired more naturally and accurately by obtaining parameters related to a zoom area to be applied to a video to be acquired by a camera based on information about the movement of an object included in a pre-recorded (e.g., pre-stored) video, and applying parameters related to the zoom area acquired from a video currently being recorded.

[0006] The technical problems to be solved by the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by ordinary technicians in the field to which the present disclosure belongs from the following description.

[0007] Technical Solution

[0008] An electronic device according to various embodiments of the present disclosure may include a camera module, a processor functionally connected to the camera module, and a memory functionally connected to the processor, wherein the memory may store instructions that, when executed, cause the processor to: acquire a first video, identify a first object in the first video, acquire parameters related to a zoom area based on movement of the first object in the first video, identify a first object corresponding to a second object included in a second video acquired through the camera module, and acquire a video of the second object from the second video based on the parameters related to the zoom area.

[0009] According to various embodiments of the present disclosure, a method for providing a video in an electronic device may include: acquiring a first video, identifying a first object in the first video, acquiring parameters related to a zoom area based on movement of the first object in the first video, identifying a first object corresponding to a second object included in a second video acquired by a camera module of the electronic device, and acquiring a video of the second object from the second video based on the parameters related to the zoom area.

[0010] Beneficial effects

[0011] By obtaining parameters related to a zoom area to be applied to a video to be acquired by a camera based on information about the movement of an object included in a pre-recorded (e.g., pre-stored) video, and applying parameters related to a zoom area acquired from a video currently being recorded, a method for providing a video and an electronic device for supporting the method according to various embodiments of the present disclosure can enable a video related to an object to be acquired more naturally and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a block diagram of an electronic device in a network environment according to various embodiments.

[0013] Figure 2 is a block diagram illustrating a camera module according to various embodiments.

[0014] Figure 3 is a block diagram illustrating an electronic device according to various embodiments.

[0015] Figure 4 is an overall flow chart illustrating a method for providing video according to various embodiments.

[0016] Figure 5 is an exemplary diagram for explaining a method for identifying a first object in a first video according to various embodiments.

[0017] Figure 6 is an exemplary diagram for explaining a padding area to be applied to a zoom area of ​​a second video according to various embodiments.

[0018] Figure 7 is an exemplary diagram for explaining a method for applying default parameters related to a zoom area to a second video according to various embodiments.

[0019] Figure 8 is an exemplary diagram for explaining a method for applying default parameters related to a zoom area to a second video according to various embodiments.

[0020] Figure 9 is a flowchart illustrating a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0021] Figure 10 is an exemplary diagram for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0022] Figure 11 is a flowchart illustrating a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0023] Figure 12 is an exemplary diagram for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0024] Figure 13 is a flowchart illustrating a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0025] Figure 14 is an exemplary diagram for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0026] Figure 15 is a flowchart illustrating a method for acquiring a first video by selecting the first video from a plurality of videos according to various embodiments.

[0027] Figure 16 is a flowchart illustrating a method for applying parameters related to a zoom area with respect to an object corresponding to a subject when displaying a preview according to various embodiments. DETAILED DESCRIPTION

[0028] Figure 1 1 is a block diagram illustrating an electronic device 101 in a network environment 100 according to various embodiments. Figure 1, the electronic device 101 in the network environment 100 can communicate with the electronic device 102 via the first network 198 (e.g., a short-range wireless communication network), or can communicate with at least one of the electronic device 104 or the server 108 via the second network 199 (e.g., a long-range wireless communication network). According to an embodiment, the electronic device 101 can communicate with the electronic device 104 via the server 108. According to an embodiment, the electronic device 101 may include a processor 120, a memory 130, an input module 150, a sound output module 155, a display module 160, an audio module 170, a sensor module 176, an interface 177, a connection terminal 178, a haptic module 179, a camera module 180, a power management module 188, a battery 189, a communication module 190, a subscriber identification module (SIM) 196, or an antenna module 197. In some embodiments, at least one of the above components (e.g., the connection terminal 178) may be omitted from the electronic device 101, or one or more other components may be added to the electronic device 101. In some embodiments, some of the above-described components (eg, sensor module 176, camera module 180, or antenna module 197) may be implemented as a single integrated component (eg, display module 160).

[0029] The processor 120 may run, for example, software (e.g., program 140) to control at least one other component of the electronic device 101 connected to the processor 120 (e.g., a hardware component or a software component), and may perform various data processing or calculations. According to one embodiment, as at least part of the data processing or calculation, the processor 120 may store a command or data received from another component (e.g., the sensor module 176 or the communication module 190) in the volatile memory 132, process the command or data stored in the volatile memory 132, and store the resultant data in the non-volatile memory 134. According to an embodiment, the processor 120 may include a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)) or an auxiliary processor 123 (e.g., a graphics processing unit (GPU), a neural processing unit (NPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operationally independent of or combined with the main processor 121. For example, when the electronic device 101 includes a main processor 121 and an auxiliary processor 123, the auxiliary processor 123 may be adapted to consume less power than the main processor 121 or be adapted to be dedicated to a specific function. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of the main processor 121.

[0030] When the main processor 121 is inactive (e.g., sleeping), the auxiliary processor 123 (rather than the main processor 121) may control at least some of the functions or states associated with at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190). Alternatively, when the main processor 121 is active (e.g., running an application), the auxiliary processor 123 may work together with the main processor 121 to control at least some of the functions or states associated with at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190). Depending on the embodiment, the auxiliary processor 123 (e.g., an image signal processor or a communication processor) may be implemented as part of another component functionally related to the auxiliary processor 123 (e.g., the camera module 180 or the communication module 190). Depending on the embodiment, the auxiliary processor 123 (e.g., a neural processing unit) may include a hardware structure dedicated to artificial intelligence model processing. The artificial intelligence model may be generated through machine learning. For example, such learning may be performed by the electronic device 101 where the artificial intelligence is executed or via a separate server (e.g., server 108). The learning algorithm may include, but is not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The artificial intelligence model may include multiple artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q network or a combination of two or more thereof, but is not limited thereto. Additionally or alternatively, the artificial intelligence model may include a software structure in addition to a hardware structure.

[0031] The memory 130 may store various data used by at least one component of the electronic device 101 (e.g., the processor 120 or the sensor module 176). The various data may include, for example, software (e.g., the program 140) and input data or output data for commands related thereto. The memory 130 may include a volatile memory 132 or a non-volatile memory 134.

[0032] The program 140 may be stored as software in the memory 130 , and may include, for example, an operating system (OS) 142 , middleware 144 , or applications 146 .

[0033] The input module 150 may receive commands or data from outside the electronic device 101 (e.g., a user) to be used by other components of the electronic device 101 (e.g., the processor 120). The input module 150 may include, for example, a microphone, a mouse, a keyboard, keys (e.g., buttons), or a digital pen (e.g., a stylus).

[0034] The sound output module 155 can output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as playing multimedia or playing records. The receiver can be used to receive incoming calls. Depending on the embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0035] The display module 160 can visually provide information to the outside of the electronic device 101 (e.g., a user). The display module 160 may include, for example, a display, a holographic device, or a projector, and a control circuit for controlling a corresponding one of the display, the holographic device, and the projector. Depending on the embodiment, the display module 160 may include a touch sensor adapted to detect a touch or a pressure sensor adapted to measure the strength of the force caused by the touch.

[0036] The audio module 170 can convert sound into an electrical signal, and vice versa. According to an embodiment, the audio module 170 can obtain sound via the input module 150, or output sound via the sound output module 155 or an earphone of an external electronic device (e.g., electronic device 102) directly (e.g., wired) or wirelessly connected to the electronic device 101.

[0037] The sensor module 176 can detect an operating state (e.g., power or temperature) of the electronic device 101 or an environmental state (e.g., a user's state) outside the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state. Depending on the embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illumination sensor.

[0038] The interface 177 may support one or more specific protocols to be used to connect the electronic device 101 directly (e.g., wired) or wirelessly to an external electronic device (e.g., the electronic device 102). Depending on the embodiment, the interface 177 may include, for example, a High-Definition Multimedia Interface (HDMI), a Universal Serial Bus (USB) interface, a Secure Digital (SD) card interface, or an audio interface.

[0039] The connection end 178 may include a connector, wherein the electronic device 101 can be physically connected to an external electronic device (e.g., the electronic device 102) via the connector. Depending on the embodiment, the connection end 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0040] The haptic module 179 may convert the electrical signal into mechanical stimulation (eg, vibration or motion) or electrical stimulation that can be recognized by the user via his sense of touch or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0041] The camera module 180 may capture still images or moving images. Depending on the embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

[0042] The power management module 188 may manage power supply to the electronic device 101. According to an embodiment, the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0043] The battery 189 may power at least one component of the electronic device 101. According to an embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0044] The communication module 190 may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108), and perform communication via the established communication channel. The communication module 190 may include one or more communication processors capable of operating independently from the processor 120 (e.g., an application processor (AP)) and supporting direct (e.g., wired) communication or wireless communication. Depending on the embodiment, the communication module 190 may include a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules can communicate with an external electronic device via a first network 198 (e.g., a short-range communication network such as Bluetooth, Wireless Fidelity (Wi-Fi) Direct, or Infrared Data Association (IrDA)) or a second network 199 (e.g., a long-range communication network such as a traditional cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or a wide area network (WAN))). These various types of communication modules can be implemented as a single component (e.g., a single chip), or these various types of communication modules can be implemented as multiple components separated from each other (e.g., multiple chips). The wireless communication module 192 can identify and authenticate the electronic device 101 in a communication network (such as the first network 198 or the second network 199) using user information (e.g., an International Mobile Subscriber Identity (IMSI)) stored in the user identification module 196.

[0045] The wireless communication module 192 can support 5G networks after 4G networks and next-generation communication technologies (e.g., new radio (NR) access technology). NR access technology can support enhanced mobile broadband (eMBB), massive machine type communication (mMTC), or ultra-reliable low-latency communication (URLLC). The wireless communication module 192 can support high-frequency bands (e.g., millimeter wave bands) to achieve, for example, high data transmission rates. The wireless communication module 192 can support various technologies for ensuring performance on high-frequency bands, such as, for example, beamforming, massive multiple-input multiple-output (massive MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, or massive antennas. The wireless communication module 192 can support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., a second network 199). According to an embodiment, the wireless communication module 192 may support peak data rates for implementing eMBB (e.g., 20 Gbps or greater), loss coverage for implementing mMTC (e.g., 164 dB or less), or U-plane latency for implementing URLLC (e.g., 0.5 ms or less for each of the downlink (DL) and uplink (UL), or 1 ms or less round trip).

[0046] Antenna module 197 can transmit or receive signals or power to or from the outside of electronic device 101 (e.g., an external electronic device). Depending on the embodiment, antenna module 197 may include an antenna comprising a radiating element formed of a conductive material or conductive pattern formed in or on a substrate (e.g., a printed circuit board (PCB)). Depending on the embodiment, antenna module 197 may include multiple antennas (e.g., an array antenna). In this case, at least one antenna suitable for the communication scheme used in a communication network (such as first network 198 or second network 199) may be selected from the multiple antennas by, for example, communication module 190 (e.g., wireless communication module 192). Signals or power can then be transmitted or received between communication module 190 and the external electronic device via the selected at least one antenna. Depending on the embodiment, additional components other than the radiating element (e.g., a radio frequency integrated circuit (RFIC)) may also be formed as part of antenna module 197.

[0047] According to various embodiments, antenna module 197 may form a millimeter wave antenna module. According to embodiments, the millimeter wave antenna module may include a printed circuit board, a radio frequency integrated circuit (RFIC), and multiple antennas (e.g., array antennas), wherein the RFIC is disposed on a first surface (e.g., bottom surface) of the printed circuit board, or adjacent to the first surface and capable of supporting a specified high frequency band (e.g., millimeter wave band), and the multiple antennas are disposed on a second surface (e.g., top surface or side surface) of the printed circuit board, or adjacent to the second surface and capable of transmitting or receiving signals in the specified high frequency band.

[0048] At least some of the above components can be connected to each other via an inter-peripheral communication scheme (e.g., a bus, general-purpose input output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and communicatively transmit signals (e.g., commands or data) therebetween.

[0049] According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 via the server 108 connected to the second network 199. Each of the electronic device 102 or the electronic device 104 may be a device of the same type as the electronic device 101, or a device of a different type than the electronic device 101. According to an embodiment, all or some operations to be executed on the electronic device 101 may be executed on one or more of the external electronic device 102, the external electronic device 104, or the server 108. For example, if the electronic device 101 should automatically execute a function or service or should execute a function or service in response to a request from a user or another device, the electronic device 101 may request the one or more external electronic devices to execute at least part of the function or service instead of executing the function or service, or the electronic device 101 may request the one or more external electronic devices to execute at least part of the function or service in addition to executing the function or service. The one or more external electronic devices that receive the request may execute at least part of the function or service requested, or execute another function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may provide the result as at least a partial reply to the request, with or without further processing the result. To this end, cloud computing technology, distributed computing technology, mobile edge computing (MEC) technology, or client-server computing technology, for example, may be used. The electronic device 101 may use, for example, distributed computing or mobile edge computing to provide ultra-low latency services. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. The server 108 may be an intelligent server using machine learning and / or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology or IoT-related technologies.

[0050] The electronic device according to various embodiments may be one of various types of electronic devices. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a household appliance. According to an embodiment of the present disclosure, the electronic device is not limited to those described above.

[0051] It should be understood that the various embodiments of the present disclosure and the terms used therein are not intended to limit the technical features set forth herein to specific embodiments, but rather include various changes, equivalents or alternative forms for corresponding embodiments. For the description of the accompanying drawings, similar reference numerals may be used to refer to similar or related elements. It will be understood that the nouns in the singular form corresponding to the term may include one or more things, unless the relevant context clearly indicates otherwise. As used herein, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C" and "at least one of A, B or C" may include any one or all possible combinations of the items listed together with the corresponding phrase in the multiple phrases. As used herein, terms such as "1st" and "2nd" or "first" and "second" may be used to simply distinguish corresponding components from another component, and do not limit the components in other aspects (e.g., importance or order). It will be understood that if an element (e.g., a first element) is referred to as being “combined with another element (e.g., a second element)”, “combined to another element (e.g., a second element)”, “connected with another element (e.g., a second element)”, or “connected to another element (e.g., a second element)”, when the term “operably” or “communicatively” is used or when the term “operably” or “communicatively” is not used, it means that the element can be directly (e.g., wired) connected to the other element, wirelessly connected to the other element, or connected to the other element via a third element.

[0052] As used in connection with various embodiments of the present disclosure, the term "module" may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with other terms (e.g., "logic," "logic block," "portion," or "circuit"). A module may be a single integrated component adapted to perform one or more functions, or the smallest unit or portion of the single integrated component. For example, depending on the embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0053] The various embodiments described herein can be implemented as software (e.g., program 140) comprising one or more instructions stored in a storage medium (e.g., internal memory 136 or external memory 138) that can be read by a machine (e.g., electronic device 101). For example, under the control of a processor, a processor (e.g., processor 120) of the machine (e.g., electronic device 101) can call at least one of the one or more instructions stored in the storage medium and execute the at least one instruction with or without the use of one or more other components. This enables the machine to be operable to perform at least one function according to the called at least one instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The machine-readable storage medium can be provided in the form of a non-transitory storage medium. The term "non-transitory" only means that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), but the term does not distinguish between data being semi-permanently stored in the storage medium and data being temporarily stored in the storage medium.

[0054] According to an embodiment, the method according to various embodiments of the present disclosure may be included and provided in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be published in the form of a machine-readable storage medium (e.g., a compact disk read-only memory (CD-ROM)), or may be published online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™), or may be distributed (e.g., downloaded or uploaded) directly between two user devices (e.g., smart phones). If published online, at least a portion of the computer program product may be temporarily generated, or at least a portion of the computer program product may be at least temporarily stored in a machine-readable storage medium (such as a manufacturer's server, an application store's server, or a memory of a forwarding server).

[0055] According to various embodiments, each component (for example, module or program) in the above-mentioned components may include a single entity or multiple entities, and some entities in the multiple entities may be separably arranged in different components. According to various embodiments, one or more components in the above-mentioned components may be omitted, or one or more other components may be added. Alternatively or additionally, multiple components (for example, module or program) may be integrated into a single component. In this case, according to various embodiments, the integrated component may still perform the one or more functions of each component in the multiple components in the same or similar manner as a corresponding component in the multiple components before integration. According to various embodiments, the operations performed by a module, program or another component may be performed sequentially, in parallel, repeatedly or in a heuristic manner, or one or more operations in the operations may be run or omitted in different orders, or one or more other operations may be added.

[0056] Figure 2 FIG2 is a block diagram 200 illustrating a camera module 180 according to various embodiments. Figure 2 The camera module 180 may include a lens assembly 210, a flash 220, an image sensor 230, an image stabilizer 240, a memory 250 (e.g., a buffer memory), or an image signal processor 260. The lens assembly 210 may collect light emitted or reflected from an object whose image is to be captured. The lens assembly 210 may include one or more lenses. Depending on the embodiment, the camera module 180 may include a plurality of lens assemblies 210. In this case, the camera module 180 may form, for example, a dual camera, a 360-degree camera, or a spherical camera. Some of the plurality of lens assemblies 210 may have the same lens properties (e.g., angle of view, focal length, autofocus, f-number, or optical zoom), or at least one lens assembly may have one or more lens properties that are different from those of another lens assembly. The lens assembly 210 may include, for example, a wide-angle lens or a telephoto lens.

[0057] The flash 220 can emit light, wherein the emitted light is used to enhance light reflected from an object. Depending on the embodiment, the flash 220 may include one or more light-emitting diodes (LEDs) (e.g., red, green, and blue (RGB) LEDs, white LEDs, infrared (IR) LEDs, or ultraviolet (UV) LEDs) or a xenon lamp. The image sensor 230 can capture an image corresponding to the object by converting light emitted from or reflected from the object and transmitted through the lens assembly 210 into an electrical signal. Depending on the embodiment, the image sensor 230 may include one image sensor selected from a plurality of image sensors having different properties (e.g., an RGB sensor, a black and white (BW) sensor, an IR sensor, or a UV sensor), a plurality of image sensors having the same properties, or a plurality of image sensors having different properties. Each image sensor included in the image sensor 230 may be implemented using, for example, a charge-coupled device (CCD) sensor or a complementary metal oxide semiconductor (CMOS) sensor.

[0058] Image stabilizer 240 can move image sensor 230 or at least one lens included in lens assembly 210 in a specific direction, or control operational properties of image sensor 230 (e.g., adjust readout timing) in response to movement of camera module 180 or electronic device 101 including camera module 180. This allows for compensating for at least a portion of negative effects (e.g., image blur) resulting from movement of the image being captured. Depending on the embodiment, image stabilizer 240 can sense such movement of camera module 180 or electronic device 101 using a gyroscope sensor (not shown) or an acceleration sensor (not shown) disposed within or outside camera module 180. Depending on the embodiment, image stabilizer 240 can be implemented as, for example, an optical image stabilizer. Memory 250 can at least temporarily store at least a portion of an image acquired via image sensor 230 for subsequent image processing tasks. For example, if multiple images are captured quickly or image capture is delayed due to shutter lag, the acquired original image (e.g., Bayer pattern image, high-resolution image) may be stored in memory 250, and its corresponding duplicate image (e.g., low-resolution image) may be previewed via display module 160. Then, if a specified condition is met (e.g., by user input or system command), at least a portion of the original image stored in memory 250 may be acquired and processed by, for example, image signal processor 260. Depending on the embodiment, memory 250 may be configured as at least a portion of memory 130, or may be configured as a separate memory that operates independently of memory 130.

[0059] The image signal processor 260 may perform one or more image processing operations on images acquired via the image sensor 230 or stored in the memory 250. The one or more image processing operations may include, for example, depth map generation, three-dimensional (3D) modeling, panoramic image generation, feature point extraction, image synthesis, or image compensation (e.g., noise reduction, resolution adjustment, brightness adjustment, blurring, sharpening, or softening). Additionally or alternatively, the image signal processor 260 may control at least one of the components included in the camera module 180 (e.g., image sensor 230) (e.g., exposure time control or readout timing control). Images processed by the image signal processor 260 may be stored back in the memory 250 for further processing, or may be provided to an external component outside the camera module 180 (e.g., memory 130, display module 160, electronic device 102, electronic device 104, or server 108). Depending on the embodiment, the image signal processor 260 may be configured as at least a portion of the processor 120, or may be configured as a separate processor that operates independently of the processor 120. If the image signal processor 260 is configured as a separate processor from the processor 120 , the at least one image processed by the image signal processor 260 may be displayed as it is by the processor 120 via the display module 160 , or may be displayed after being further processed.

[0060] Depending on the embodiment, the electronic device 101 may include multiple camera modules 180 having different properties or functions. In this case, at least one of the multiple camera modules 180 may form, for example, a wide-angle camera, and at least another of the multiple camera modules 180 may form a telephoto camera. Similarly, at least one of the multiple camera modules 180 may form, for example, a front-facing camera, and at least another of the multiple camera modules 180 may form a rear-facing camera.

[0061] Figure 3 is a block diagram 300 illustrating an electronic device 101 according to various embodiments.

[0062] The electronic device 101 may include a processor 120 , a memory 130 , a display module 160 , a communication circuit 195 , and / or a camera module 180 .

[0063] In an embodiment, the processor 120 may control the operation of the memory 130, the display module 160, the communication circuit 195, and / or the camera module 180. The processor 120 may execute the instructions 310 stored in the memory 130. The processor 120 may control the communication circuit 195 so that the communication circuit 195 transmits and receives RF signals.

[0064] In an embodiment, the memory 130 may store instructions 310. The instructions 310 may configure the operation of the display module 160 and the camera module 180. The instructions 310 may include object detection instructions 311, object tracking instructions 312, zoom control instructions 313, and image reconstruction instructions 314. The instructions 310 are primarily described as being executed by the processor 120, and at least one of the instructions 310 may be executed by a processor included in the camera module 180 (e.g., Figure 2 The image signal processor 260 in is executed.

[0065] In an embodiment, the display module 160 can display an image. The display module 160 may include a user interface 161. The user interface 161 may include a touch interface (e.g., a touch sensor) configured to receive a user's touch input. The display module 160 may display a graphical user interface that can visually provide information to the user or receive user input.

[0066] In an embodiment, the communication circuit 195 may be a Figure 1 The wireless communication circuit 192 in the communication module 190 is basically the same component.

[0067] In an embodiment, the camera module 180 (e.g., Figure 2 The lens assembly 210 in the camera module 180 can capture an image of the external environment. The camera module 180 can capture an image of at least one person. The camera module 180 can obtain a visual image of the external environment. The camera module 180 can obtain light incident from the external environment.

[0068] In an embodiment, the camera module 180 (e.g., Figure 2 The image sensor 230 in the image sensor module 180 can convert the external environment into image data based on the acquired light. The processor 120 can receive the image data converted by the camera module 180 and display it on the display module 160.

[0069] In an embodiment, the processor 120 may detect an object in the image data based on the object detection instructions 311. The processor 120 may detect a specific object (e.g., a person) in the image data based on the object detection instructions 311. The processor 120 may simultaneously detect multiple objects in the image data based on the object detection instructions 311.

[0070] In an embodiment, the processor 120 may track an object from the image data based on the object tracking instructions 312. The processor 120 may track a moving object from the image data based on the object tracking instructions 312. The processor 120 may track an object from the image data based on the object tracking instructions 312. The processor 120 may select and track an object of high interest to the user from the image data based on the object tracking instructions 312. The processor 120 may configure an object to be tracked as a focus subject. The processor 120 may send a control signal for the camera module 180 to track the object. For example, the camera module 180 may change the direction in which the lens assembly 210 faces based on the control signal.

[0071] In an embodiment, the processor 120 may track an object from image data by moving at least a portion of the electronic device 101. For example, the electronic device 101 (e.g., an electronic device implemented in the form of a robot) may further include a drive unit (e.g., a motor) that may move the position of the electronic device 101 or the direction in which the mobile electronic device 101 (e.g., the camera module 180) is facing. When the object moves or the focused object changes, the processor 120 may control the drive unit to track the moving object or the focused object. As another example, the electronic device 101 may be mounted on an external electronic device (e.g., a gimbal) that is capable of moving the electronic device 101. When the object moves or the focused object changes, the processor 120 may send a control signal for moving the electronic device to the external electronic device so as to track the moving object or the focused object.

[0072] In an embodiment, the processor 120 may zoom in (zoom in) the captured visual image of the external environment based on the zoom control instruction 313. The processor 120 may magnify at least a portion of the captured visual image of the external environment based on the zoom control instruction 313. The processor 120 may configure an area as a zoom area, which is desired to be magnified in the captured visual image of the external environment based on the zoom control instruction 313. The processor 120 may configure at least one zoom area based on the zoom control instruction 313. The processor 120 may control the magnification of the zoom area based on the zoom control instruction 313. The processor 120 may send a control signal related to zooming in or out (zooming out) to the camera module 180. The camera module 180 may perform zooming in or out by changing the configuration of the lens assembly 210 based on the received control signal. Without limitation, those skilled in the art will readily appreciate that the camera module 180 may perform zooming in or out by using the image signal processor 260.

[0073] In an embodiment, the processor 120 may configure a zoom region based on the zoom control instruction 313 so that the zoom region includes an object that is desired to be magnified and displayed. The processor 120 may control the lens assembly 210 based on the zoom control instruction 313 so that the lens assembly 210 magnifies an object in the image data that is of high interest to the user. The lens assembly 210 may magnify the object in the image data based on the zoom control instruction 313 to capture an image thereof. The processor 120 may configure the object that is desired to be magnified as a focused subject. The lens assembly 210 may magnify the focused subject under the control of the processor 120.

[0074] In an embodiment, when the electronic device 101 (e.g., an electronic device implemented in the form of a robot) includes a drive unit (e.g., a motor) that can move the position of the electronic device 101 or the direction in which the electronic device 101 (e.g., the camera module 180) faces, the processor 120 can control the drive unit so that the object in the video remains at a specified size (or the object is included in the video). For example, when the subject approaches the electronic device 101, the object corresponding to the subject is not included in the viewing angle of the camera module 180, the processor 120 can move the position of the electronic device 101 through the drive unit so that the electronic device 101 moves away from the subject so that the object can remain at a specified size in the video.

[0075] In an embodiment, when the electronic device 101 is mounted on an external electronic device (e.g., a gimbal) capable of moving the electronic device 101, the processor 120 may control the external electronic device so that an object in the video is maintained at a specified size (or the object is included in the video). For example, when an object corresponding to the subject is not included in the viewing angle of the camera module 180 when the subject approaches the electronic device 101, the processor 120 may send a signal to move the external electronic device away from the subject through the communication module 190 so that the object in the video is maintained at a specified size.

[0076] In an embodiment, the processor 120 may reconstruct an image based on the image reconstruction instructions 314. The processor 120 may display the reconstructed image on the display module 160 or store it in the memory 130. The processor 120 may display an image with an enlarged zoom area on the display module 160 or store it in the memory 130 based on the image reconstruction instructions 314. Alternatively, the processor 120 may display an image with a cropped zoom area on the display module 160 or store it in the memory 130 based on the image reconstruction instructions 314. The processor 120 may display an image with rearranged zoom areas on the display module 160 based on the image reconstruction instructions 314. The processor 120 may display an image with an emphasized zoom area on the display module 160 based on the image reconstruction instructions 314. The processor 120 may display an image that moves according to an object moving in the zoom area on the display module 160 based on the image reconstruction instructions 314.

[0077] The electronic device according to various embodiments of the present disclosure may include a camera module 180, a processor 120 functionally connected to the camera module 180, and a memory functionally connected to the processor 120, wherein the memory 130 may store instructions that, when executed, cause the processor 120 to obtain a first video, identify a first object in the first video, obtain parameters related to a zoom area based on movement of the first object in the first video, identify a first object corresponding to a second object included in a second video obtained by the camera module 180, and obtain a video of the second object from the second video based on the parameters related to the zoom area.

[0078] In various embodiments, the instructions may cause the processor 120 to determine a zoom region associated with the second object from the second video based on parameters associated with the zoom region.

[0079] In various embodiments, the parameter related to the zoom area may include at least one of a threshold related to movement of the zoom area, an average movement speed of the zoom area, or a fill area of ​​the zoom area.

[0080] In various embodiments, the instructions may cause the processor 120 to determine a size of the zoom region based on the fill region, and to move the zoom region based on an average movement speed when the second object moves a threshold value or more.

[0081] In various embodiments, the instructions may cause processor 120 to identify an object at a location corresponding to the location of the second object as the first object in the first video.

[0082] In various embodiments, the instructions may cause the processor 120 to identify a first time corresponding to a second object in the second video, and identify a first object corresponding to a second time in the first video based on the identified first time.

[0083] In various embodiments, the electronic device 101 may also include a microphone (e.g., input module 150), and the instructions may cause the processor 120 to obtain an audio signal through the microphone, identify a second time corresponding to the first time based on the audio signal and an audio signal included in the first video, and identify an object included in the first video at the second time as the first object.

[0084] In various embodiments, the instructions may cause the processor 120 to identify at least one of the time, beat, rhythm, or pace of the acquired audio signal, and identify a second time corresponding to the first time based on at least one of the identified time, beat, rhythm, and pace.

[0085] In various embodiments, the processor 120 may be caused to recognize a voice signal included in the acquired audio signal, and identify a second time corresponding to the first time based on the recognized voice signal.

[0086] In various embodiments, the instructions may cause the processor 120 to identify whether the first video includes a plurality of objects, and when the first video includes a plurality of objects, identify a first object corresponding to a second object among the plurality of objects.

[0087] In various embodiments, the instructions may cause the processor 120 to identify a second time point of the first video corresponding to a first time point of the second video, determine a position and size of a first object included in the first video at a third time point after the second time point of the first video, determine a position and size of a zoom area based on the determined position and size of the first object and parameters related to the zoom area, and obtain a video of the second object from the second video based on the determined position and size of the zoom area.

[0088] In various embodiments, the instructions may cause the processor 120 to determine the size of a first object respectively included in multiple images of the first video, and determine the position and size of the zoom area based on the size of the first object and parameters related to the zoom area, wherein the multiple images are at a time point from a first time before a second time relative to the first video to a time point of a second time after the second time point.

[0089] In various embodiments, the instructions may cause the processor 120 to identify a second time point corresponding to the first time point based on the position of the second object and the position of the first object or at least one of an audio signal acquired by a microphone of the electronic device 101 and an audio signal included in the first video.

[0090] In various embodiments, the instructions may cause the processor 120 to adjust the configuration of the camera module 180 based on the parameters related to the zoom area, and control the camera module 180 to acquire a video of the second object based on the adjusted configuration of the camera module 180 .

[0091] Figure 4 is an overall flow chart 400 illustrating a method for providing video according to various embodiments.

[0092] Reference Figure 4 In an embodiment, in operation 401, the processor 120 may obtain a first video (hereinafter referred to as a "first video"). In an embodiment, the first video may also be referred to as a "reference video."

[0093] In an embodiment, the first video may be a video stored in the memory 130 before a video (eg, a second video to be described later) is acquired (eg, captured) by the camera module 180. The processor 120 may acquire the first video from the memory 130.

[0094] In an embodiment, the first video may be a video acquired in real time by using a streaming service via the communication module 190. The processor 120 may acquire the first video through the communication module 190 from the outside (eg, a server providing a streaming service).

[0095] However, the method for acquiring the first video is not limited to the above example.

[0096] In an embodiment, when a plurality of videos are stored in the memory 130, the processor 120 may automatically (for example, in a specified manner) or based on user input select a first video from the plurality of videos to obtain the first video. Various embodiments of the method for obtaining the first video by selecting the first video from the plurality of videos will be described later. Figure 15 Provide a detailed description.

[0097] According to an embodiment, in operation 403 , the processor 120 may recognize (eg, detect) a first object (hereinafter, referred to as “first object”) in a first video.

[0098] In an embodiment, the processor 120 may identify at least one first object corresponding to at least one subject (e.g., a person) in a plurality of images (e.g., a plurality of image frames) of the first video. For example, the processor 120 may detect the at least one object from the plurality of images of the first video using a specified algorithm (e.g., a person detection algorithm or a face detection algorithm).

[0099] The following will refer to Figure 5Various embodiments of operations for identifying a first object in a first video are described in detail.

[0100] Figure 5 is an exemplary diagram 500 for explaining a method for identifying a first object in a first video according to various embodiments.

[0101] Reference Figure 5 In an embodiment, the processor 120 may identify (e.g., detect) a first object 511 from the image 510 of the first video. When the first object 511 is identified from the image 510 of the first video, the processor 120 may determine (e.g., configure) a tracking area 513 for the first object 511. The tracking area 513 may be in the form of a bounding box that surrounds the first object within the image 510 of the first video. However, the form of the tracking area 513 is not limited thereto.

[0102] In an embodiment, the processor 120 may identify a first object 521 from an image 520 of a first video. The processor 120 may obtain (extract) feature points (e.g., key points) associated with a skeleton of the first object 521 (e.g., an object corresponding to a person) within the image 520 of the first video. The processor 120 may determine a skeleton 522 of the first object 521 by connecting the feature points based on the feature points associated with the skeleton 522. The processor 120 may determine a tracking area 523 surrounding the skeleton 522 of the first object 521 based on the skeleton 522 of the first object 521.

[0103] In an embodiment, the processor 120 may identify multiple first objects 531-1 and 531-2 from the image 530 of the first video. When the multiple first objects 531-1 and 531-2 are identified from the image 530 of the first video, the processor 120 may determine tracking areas for the multiple first objects 531-1 and 531-2. For example, the processor 120 may determine corresponding tracking areas 533-1 and 533-2 for the multiple first objects 531-1 and 531-2. As another example, the processor 120 may determine a tracking area (not shown) for an area (e.g., a union of areas including the multiple first objects 531-1 and 531-2, respectively). In an embodiment, the position and size of the first object may correspond to the position and size of the tracking area for the first object. hereinafter, it will be understood that the description of the position and size of the first object includes the description of the position and size of the tracking area for the first object.

[0104] Return to reference Figure 4In operation 405, in an embodiment, the processor 120 may obtain parameters (hereinafter, interchangeably used with "default parameters" or "default parameters related to the zoom area") based on the movement of the first object in the first video. The default parameters may include parameters to be applied (or configured) to the second video. For example, the default parameters may include at least one of a threshold value (first threshold value) associated with the movement of the zoom area, a movement speed of the zoom area of ​​the second video, and / or a fill area (e.g., a size of the fill area) to be applied to the zoom area of ​​the second video.

[0105] In an embodiment, the processor 120 may obtain information about the movement of the first object from the first video. In an embodiment, the information about the movement of the first object may include at least one of an area in which the first object has moved in the first video, an average movement speed (and / or a maximum movement speed and a minimum movement speed) of the first object, or a change in the position and size of the first object.

[0106] According to an embodiment, the area where the first object has moved within the first video may include areas of multiple images of the first object in which the first object has been located (e.g., moved). For example, the area where the first object has moved within the first video may be the union of areas of multiple images of the first video in which the first object has been located.

[0107] In an embodiment, the average movement speed of the first object may be a value obtained by adding the amount of change in the position of the first object in the multiple images of the first video (for example, the movement distance of the first object) and dividing it by the time during which the multiple images of the first video are acquired. In an embodiment, the maximum movement speed of the first object may be the maximum amount of change per unit time (for example, the time interval during which each of the multiple images is acquired) in the position of the first object in the multiple images of the first video. In an embodiment, the minimum movement speed of the first object may be the minimum amount of change per unit time in the position of the first object in the multiple images of the first video.

[0108] In an embodiment, the amount of change in the position and size of the first object (e.g., the average amount of change in the position and size of the first object) may include the number of movements of the first object (e.g., the number of movements detected within a specified time). For example, as the number of changes in the position of the subject (e.g., changes in the up / down / left / right position of the subject in multiple images) increases within a specified time, the amount of change in the position of the first object corresponding to the subject may increase. As another example, as the number of movements of the subject approaching or moving away from the electronic device 101 (e.g., the camera module 180) increases, the amount of change in the size of the first object, by which it increases or decreases, may increase. In an embodiment, the amount of change in the position and size of the first object may be referred to as the movement variance of the first object.

[0109] In an embodiment, in a case where a plurality of first objects are included in the first video, the processor 120 may obtain information on movement of each of the plurality of first objects.

[0110] In an embodiment, when a plurality of first objects are included in the first video, the processor 120 may obtain information about the movement of all of the plurality of first objects. Figure 5 When the first video includes multiple first objects, as in image 530 in FIG, the processor 120 may determine a tracking area for an area including all of the multiple first objects (e.g., a union of areas including the multiple first objects). The processor 120 may obtain information about the movement of all of the multiple first objects by obtaining information about changes in the determined tracking area.

[0111] In an embodiment, in a case where a plurality of first objects are included in the first video, the processor 120 may acquire information about movement of each of the plurality of first objects and information about movement of all of the plurality of first objects.

[0112] In an embodiment, the processor 120 may obtain parameters related to the zoom area based on the information obtained about the movement of the first object. For example, the processor 120 may obtain parameters related to the zoom area to be applied to the video to be acquired by the camera module 180 (hereinafter referred to as "second video") based on the information obtained about the movement of the first object.

[0113] In an embodiment, the processor 120 may obtain a threshold value (hereinafter referred to as a "first threshold value") related to the movement of the zoom area to be applied to the second video based on the area in which the first object has moved within the first video. For example, the processor 120 may determine (e.g., configure) the first threshold value to be lower as the area in which the first object has moved within the first video becomes larger (e.g., wider). The processor 120 may also determine (e.g., configure) the first threshold value to be higher as the area in which the first object has moved within the first video becomes smaller (e.g., narrower).

[0114] In an embodiment, the first threshold value may be a value used to determine whether to move the position of the zoom area of ​​the first video in a second video that is subsequent to the first video (e.g., following the first video). For example, if the position difference between the position of an object included in the first video and the position of an object included in the second video that is subsequent to the first video is less than or equal to the first threshold value, the processor 120 may maintain (unchanged) the position of the zoom area of ​​the first video in the second video (e.g., such that the zoom area does not move).

[0115] In an embodiment, by determining the first threshold to be lower as the area where the first object has moved in the first video becomes larger, the processor 120 can quickly (or sensitively) track the object to be identified in the second video. For example, if there is movement of the object to be identified in the second video, by determining the first threshold to be lower, the processor 120 can quickly move the zoom area according to the movement of the object.

[0116] In an embodiment, by determining the second threshold to be higher as the area where the first object has moved within the first video becomes smaller, the area processor 120 can keep the zoom area of ​​the object unchanged relative to small movements of the object to be recognized within the second video. The processor 120 can obtain a stable video by keeping the zoom area of ​​the object unchanged relative to small movements of the object to be recognized within the second video.

[0117] In an embodiment, the processor 120 may determine the movement speed of the zoom area of ​​the second video based on the average movement speed of the first object. For example, the processor 120 may configure the movement speed of the zoom area of ​​the second video to be higher (e.g., faster) as the average movement speed of the first object is higher (e.g., faster), and determine the movement speed of the zoom area of ​​the first video to be lower (e.g., slower) as the average movement speed of the first object is lower (e.g., slower).

[0118] In an embodiment, by determining the movement speed of the zoom area of ​​the second video based on the average movement speed of the first object, the processor 120 can stably or accurately track the object to be identified in the second video. For example, by determining the movement speed of the zoom area of ​​the second video based on the average movement speed of the first object, the processor 120 can smoothly or accurately move the zoom area of ​​the object to be identified in the second video.

[0119] In an embodiment, the processor 120 may determine a padding area (eg, a size of the padding area) to be applied to the zoom area of ​​the second video based on the amount of change in the position and size of the first object.

[0120] Figure 6 is an exemplary diagram 600 for explaining a padding area to be applied to a zoom area of ​​a second video according to various embodiments.

[0121] Reference Figure 6, the size of the zoom area 610 may be determined by the size of the tracking area 620 (or the size of the object) and the left distance (or left padding (PL)), top distance (or top padding (PU)), right distance (or right padding (PR)), and bottom distance (or bottom padding (PD)) from the tracking area 620. In an embodiment, the processor 120 may determine the padding area of ​​the zoom area to be applied to the second video to become wider as the amount of change in the position and size of the first object of the first video increases. For example, the processor 120 may determine the left padding (PL), top padding (PU), right padding (PR), and bottom padding (PD) of the zoom area to be applied to the second video to become longer as the average amount of change in the position of the first object of the first video and / or the size of the first object increases. The processor 120 may determine the padding area of ​​the zoom area to be applied to the second video to become narrower as the amount of change in the size of the first object of the first video and / or the position of the first object decreases. In an embodiment, in the case where there is a large amount of movement of the object to be identified in the second video within a specified time period, the processor can keep the size of the zoom area to be applied to the second video unchanged by determining the fill area of ​​the zoom area to be applied to the second video to become wider as the amount of change in the position and size of the first object of the first video increases. By keeping the size of the zoom area to be applied to the second video unchanged, the processor 120 can minimize the jitter of the video acquired through the zoom area to be applied to the second video. In an embodiment, in the case where there is a small amount of movement of the object to be identified in the second video within a specified time period, the processor can acquire a video including an object of larger size (for example, magnified by a larger magnification) through the zoom area to be applied to the second video by determining the fill area of ​​the zoom area to be applied to the second video to become narrower as the amount of change in the position and size of the first object of the first video decreases.

[0122] In an embodiment, in a case where a plurality of first objects are included in the first video, the processor 120 may obtain a parameter related to a zoom area of ​​each of the plurality of first objects based on information about movement of each of the plurality of first objects.

[0123] In an embodiment, in a case where a plurality of first objects are included in the first video, the processor 120 may obtain parameters related to zoom areas of all of the plurality of first objects based on information about movements of all of the plurality of first objects.

[0124] In an embodiment, in the case where multiple first objects are included in the first video, the processor 120 can obtain parameters related to the zoom area of ​​each of the multiple first objects and parameters related to the zoom area including all of the multiple first objects based on information about the movement of each of the multiple first objects and information about the movement of all of the multiple first objects.

[0125] In an embodiment, in operation 407 , the processor 120 may recognize a first object corresponding to a second object (hereinafter, referred to as “second object”) included in a second video acquired through the camera module 180 .

[0126] In an embodiment, the processor 120 may detect a second object from the second video acquired by the camera module 180. For example, the processor 120 may detect at least one second object from a plurality of images of the second video by using a specified algorithm (e.g., a human detection algorithm or a face detection algorithm).

[0127] In an embodiment, the processor 120 may identify a first object within the first video that corresponds to a second object of the second video.

[0128] In an embodiment, the processor 120 may identify a first object corresponding to a second object by comparing features (e.g., feature points) of the second object with features of the first object in the first video. For example, in a case where the similarity (or degree of match) between the features of the second object and the features of the first object in the first video is greater than or equal to a specified similarity, the processor 120 may identify the first object as corresponding to the second object. As another example, in a case where the first video includes multiple first objects, the processor 120 may identify a first object corresponding to the second object from the multiple first objects by comparing the second object and the multiple first objects. As another example, in a case where the second video includes multiple second objects and the first video includes multiple first objects, the processor 120 may identify at least one first object from the multiple first objects corresponding to at least one second object selected from the multiple second objects based on user input.

[0129] In an embodiment, the processor 120 may identify the first object corresponding to the second object by comparing the position of the second object with the position of the first object. For example, the processor 120 may compare the position (e.g., coordinates) of the second object at a first time point in the second video (e.g., a time point after a first time relative to a start time point (e.g., a capture time point) of the second video) with the position of the first object at a time point in the first video corresponding to the first time point of the second video (e.g., a time point after a first time relative to a start time point of the first video). If the difference between the position of the second object at the first time point in the second video and the position of the first object at the time point corresponding to the first time point is less than a specified difference, the processor 120 may identify the first object as corresponding to the second object.

[0130] In an embodiment, the processor 120 may identify the first object corresponding to the second object by comparing the amount of change in the position of the second object with the amount of change in the position of the first object. For example, the processor 120 may identify a change (e.g., displacement) in the position of the second object within a predetermined number of images in the second video. The processor 120 may identify an object whose position has changed due to a change in the position of the second object within the images of the first video as the first object corresponding to the second object.

[0131] In an embodiment, when the first video includes multiple first objects, by comparing the position (or the change in position) of the second object with the positions (or the change in position) of the multiple first objects, the processor 120 can identify the first object corresponding to the second object from the multiple first objects.

[0132] In an embodiment, the processor 120 may identify the first object corresponding to the second object based on a time point (or time) corresponding to the first video and the second video.

[0133] For example, the processor 120 may identify the second object at a first time point in the second video (e.g., a time point after the first time point based on the start time point of the second video). The processor 120 may identify a time point in the first video corresponding to the first time point in the second video (e.g., a time point after the first time point from the start time of the first video). The processor 120 may identify an object included in an image in the first video at a time point corresponding to the first time point in the second video as a first object corresponding to the second object.

[0134] As another example, the processor 120 may identify, in the second video, a second object included in the second video for the first time (e.g., a second object corresponding to a subject that is outside the viewing angle of the camera module 180 after being outside the viewing angle of the camera module 180 for the first time). The processor 120 may identify, in the first video, an object included in the first video for the first time as the first object corresponding to the second object.

[0135] In an embodiment, the processor 120 may identify a first object corresponding to a second object based on the audio signal.

[0136] In an embodiment, the processor 120 may acquire an audio signal (e.g., an audio signal related to music) (hereinafter referred to as "the acquired audio signal") through a microphone (e.g., the input module 150) while acquiring the second video through the camera module 180. The processor 120 may identify the waveform of the acquired audio signal and / or the energy of the audio signal.

[0137] In an embodiment, the processor 120 may identify the correlation between the acquired audio signal and the audio signal of the first video based on the waveform of the acquired audio signal and the waveform of the audio signal of the first video. The processor 120 may identify the acquired audio signal and the audio signal of the first video as corresponding audio signals (for example, audio signals for the same music) based on the correlation between the acquired audio signal and the audio signal of the first video. The processor 120 may identify the second time point (for example, the second time point based on the time point of the first video) of the first video corresponding to the first time point of the second video (for example, the first time point based on the time point of the second video) based on the time difference (for example, delay time or phase difference) between the waveform of the acquired audio signal and the waveform of the audio signal of the first video.

[0138] In an embodiment, the processor 120 may identify, based on the energy of the acquired audio signal and the energy of the audio signal of the first video, that the energy of the acquired audio signal and the energy of the audio signal of the first video are corresponding audio signals (e.g., audio signals of the same music). The processor 120 may identify, based on the time difference (e.g., delay time) between the energy of the acquired audio signal and the energy of the audio signal of the first video, a second time point (e.g., a second time point based on the time point of the first video) relative to a first time point (e.g., a first time point based on the time point of the second video).

[0139] In an embodiment, the processor 120 may identify a first object corresponding to a second object based on at least one of the time, beat, rhythm, or pace of the acquired audio signal. For example, the processor 120 may identify at least one of the time, beat, rhythm, or pace of the acquired audio signal at a first time point in the second video. The processor 120 may identify, in the first video, a time point corresponding to at least one of the time, beat, rhythm, or pace of the acquired audio signal as a time point corresponding to (or synchronized with) the first time point in the second video.

[0140] In an embodiment, the processor 120 may identify a first object corresponding to a second object based on a voice signal included in the acquired audio signal. For example, the processor 120 may identify a voice signal (e.g., a user's voice signal) included in the acquired audio signal at a first time point in the second video, for example, by using a voice recognition module. The processor 120 may identify the time point in the first video at which a voice signal corresponding to the voice signal included in the acquired audio signal is recognized as a time point corresponding to the first time point in the second video.

[0141] In an embodiment, the processor 120 may identify the first object corresponding to the second object based on the noise (e.g., ambient noise or white noise) included in the acquired audio signal. For example, the processor 120 may identify the noise included in the acquired audio signal at a first time point in the second video. The processor 120 may identify the time point at which a voice signal corresponding to the noise included in the acquired audio signal is recognized in the first video as a time point corresponding to the first time point in the second video.

[0142] In an embodiment, by comparing features (e.g., feature points) of a second object in a second video with features of a first object in a first video, the processor 120 may identify a first object corresponding to the second object at a first time point in the second video and a second time point corresponding thereto.

[0143] In an embodiment, by comparing the position (or the change in position) of the second object with the position (or the change in position) of the first object, the processor 120 can identify the first object corresponding to the second object at a first time point in the second video and a second time point corresponding to the first time point.

[0144] In an embodiment, by comparing the features and position (or the amount of change in position) of the second object with the features and position (or the amount of change in position) of the first object, the processor 120 can identify the first object corresponding to the second object at a first time point in the second video and a second time point corresponding to the first time point.

[0145] The embodiment of operation 407 illustrates identifying a first object corresponding to a second object included in a second video, but is not limited thereto. For example, the processor 120 may identify a second object in the second video corresponding to a first object included in the first video. As another example, if multiple first objects are identified in the first video, the processor 120 may identify a second object in the second video corresponding to each of the multiple first objects.

[0146] In an embodiment, in operation 409, the processor 120 may obtain a video of the second object from the second video based on parameters related to the zoom area (e.g., default parameters related to the zoom area). For example, the processor 120 may obtain the video of the second object by cropping a plurality of images included in the second video based on the default parameters.

[0147] In an embodiment, the processor 120 may apply default parameters associated with the zoom area of ​​the first object corresponding to the second object in the first video for the second object (or multiple images including the second object) in the second video. For example, in a case where the second object is the 2-1st object, the processor 120 may apply default parameters associated with the zoom area of ​​the 1-1th object corresponding to the 2-1st object in the first video. For another example, in a case where the second object is the 2-2nd object, the processor 120 may apply default parameters associated with the zoom area of ​​the 1-2th object corresponding to the 2-2nd object in the first video. As another example, in a case where the second object is multiple second objects, the processor 120 may apply default parameters associated with the zoom areas of multiple first objects corresponding to the multiple second objects in the first video.

[0148] In an embodiment, for each time segment of the second video, based on the second object included in the second video during the time segment, the processor 120 may apply default parameters related to the zoom area of ​​the first object in the first video corresponding to the second object to the second object. For example, if the image in the first time segment of the second video includes the 2-1st object, the processor 120 may apply default parameters related to the zoom area of ​​the 1-1st object in the first video corresponding to the 2-1st object. In another example, if the image in the second time segment of the second video includes the 2-2nd object, the processor 120 may apply default parameters related to the zoom area of ​​the 1-2nd object in the first video corresponding to the 2-2nd object.

[0149] In an embodiment, the processor 120 may apply default parameters related to the zoom area to the second video (eg, each of a plurality of images included in the second video) while acquiring the second video.

[0150] In an embodiment, the processor 120 may determine a threshold (first threshold) related to the movement of the zoom area to be applied to the second video, a moving speed of the zoom area of ​​the second video, and / or a fill area of ​​the zoom area to be applied to the second video (e.g., the size of the fill area) as default parameters related to the zoom area.

[0151] In an embodiment, while acquiring the second video, the processor 120 may acquire the video by moving the zoom area having a size determined based on the padding area at a moving speed (e.g., an average moving speed) of the zoom area when the second object moves a first threshold or more.

[0152] The following will refer to Figure 7 and Figure 8 Various embodiments of a method for applying default parameters related to a zoom area for a second video are described in detail.

[0153] Figure 7 is an exemplary diagram 700 for explaining a method for applying default parameters related to a zoom area for a second video according to various embodiments.

[0154] Reference Figure 7 , reference numeral 710 may indicate a plurality of images 711 , 712 , and 713 in a second video acquired through the camera module 180 over time.

[0155] In an embodiment, the processor 120 may apply (e.g., configure) default parameters to the second object 721 detected from the first image 711 at the first time point (t1). For example, the processor 120 may apply the default parameters to the fill area of ​​the zoom area for the second object 721 (or the tracking area of ​​the second object 721) detected from the first image 711 at the first time point (t1), thereby determining (e.g., configuring) the first zoom area 731.

[0156] In an embodiment, the processor 120 may apply (e.g., configure) default parameters to the second object 722 detected from the second image 712 at the second time point (t2). For example, the processor 120 may apply the default parameters to the movement speed of the zoom region for the first zoom region 731 of the first image 711 at the first time point (t1). The processor 120 may apply the default parameters to the fill area of ​​the zoom region to the detected second object 722 (or the tracking area of ​​the second object 722), thereby determining (e.g., configuring) the second zoom region 732. In an embodiment, if the difference between the position of the first object 721 in the first image 711 at the first time point (t1) and the position of the second object 722 in the second image 712 at the second time point (t2) is a first threshold or less, the processor 120 may not move the position of the first zoom region 731 to the position of the second zoom region 732 relative to the second image 712 at the second time point (t2) (e.g., the position of the zoom region 731 at the first time point (t1) is maintained at the second time point (t2)).

[0157] In an embodiment, the processor 120 may apply (eg, configure) default parameters to the second object 723 detected from the third image 713 at the third time point ( t3 ), thereby determining the third zoom area 733 .

[0158] In an embodiment, the processor 120 may zoom in (or out) the first zoom area 731, the second zoom area 732, and the third zoom area 733, thereby acquiring a portion of the video. Figure 7The described method repeats the operation of acquiring the first zoom area 731 , the second zoom area 732 , and the third zoom area 733 , thereby acquiring a video.

[0159] exist Figure 7 , an example of a method for applying default parameters based on the second object 722 detected from the second image 712 of the second video has been described. However, in an embodiment, the processor 120 may determine (e.g., predict) the position and size of an object to be included in the second image 812 by using the first video, and apply the default parameters to the determined position and size of the object. Figure 9 and Figure 10 Various embodiments of a method for applying default parameters based on the position and size of an object to be included in the second image 812 by using the first video are described in detail.

[0160] Figure 8 800 is an exemplary diagram for explaining a method of applying default parameters related to a zoom area to a second video according to various embodiments.

[0161] Reference Figure 8 , reference numeral 810 may indicate a plurality of images 811 , 812 , and 813 in a second video acquired through the camera module 180 over time.

[0162] In an embodiment, based on the position and size of the object 821 in the first image 811 acquired at the first time point (t1) and the position and size of the object in at least one image acquired before the first image 811, the processor 120 may determine (e.g., predict) the position and size of the object 822 in the second image 812 acquired at the second time point (t2) after the plurality of images 813. For example, based on the position and size of the object 821 in the first image 811 and the position and size of the object in at least one image acquired before the first image 811, the processor 120 may determine (e.g., predict) the position and size of the object 822 in the second image 812 after the plurality of images 813 by using a specified method (e.g., a Kalman filter).

[0163] In an embodiment, the processor 120 may determine (e.g., calculate) the second time point (t2) (or the number of multiple images 813 corresponding to the second time point (t2)) by considering the speed at which images of the second video are acquired (e.g., frames per second (FPS)) and the time required to determine the zoom area for the multiple images 813.

[0164] In an embodiment, based on the position and size of the object 821 in the first image 811 and the position and size of the object 822 in the second image 812, by using default parameters related to the zoom area of ​​the first object in the first video, the processor 120 can determine the position and size of the zoom area to be applied to the multiple images 813 to be acquired between the first image 811 and the second image 812.

[0165] In an embodiment, the processor 120 may determine a zoom region (e.g., the position and size of the zoom region) 832 to be applied to the object 822 based on the position and size of the object 822 in the second image 812. For example, the processor 120 may determine the zoom region 832 by applying a fill area of ​​the zoom region with default parameters based on the position and size of the object 822 in the second image 812.

[0166] In an embodiment, based on the zoom area 831 of the object 821 in the first image 811 and the zoom area 832 of the object 822 in the second image 812, the processor 120 can determine a zoom area (hereinafter referred to as a "target zoom area") to be applied to multiple images 813 to be acquired between the first image 811 and the second image 812 (e.g., zoom area 833).

[0167] In an embodiment, the processor 120 may determine parameters related to the size and movement speed of the target zoom area by using Equation 1 below.

[0168] Equation 1

[0169] k=α+β * area(B1)+γ * (1-IoU(B1,B2)+μ * distance(B1, B2)

[0170] In [Equation 1], k may represent a parameter related to the moving speed of the target zoom area, B1 may represent the position and size of the zoom area 831 of the first image 811 , and B2 may represent the position and size of the zoom area 832 of the second image 812 .

[0171] Area(B1) may represent the size (e.g., area) of the zoom region 831 of the first image 811. The intersection over union (IoU) (B1, B2) may represent the intersection relative to the union of B1 and B2 (a value obtained by dividing the intersection by the union). Distance(B1, B2) may represent the distance between the position of B1 and the position of B2 (e.g., the difference between the position of B1 and the position of B2).

[0172] α may be a coefficient indicating the moving speed of the zoom area of ​​the default parameter, β may be a coefficient indicating the weight of the zoom area for the 1-1th time point of the second video, γ may be a coefficient indicating the weight for IoU(B1, B2), and μ may be a coefficient indicating the weight for distance(B1, B2).

[0173] α, β, γ, and μ can be configured so that k is between 0 and 1.

[0174] Based on [Equation 1], the moving speed of the target zoom area can be proportional to the moving speed of the zoom area of ​​the default parameters, the size (e.g., area) of the zoom area 831 of the first image 811, and the distance between the zoom area 831 and the zoom area 832, and can be inversely proportional to IoU (B1, B2) (the overlapping area between the zoom area 831 and the zoom area 832).

[0175] In an embodiment, based on [Equation 1], as the distance between zoom area 831 and zoom area 832 increases (e.g., as they become further apart), the movement speed of the target zoom area may increase. Based on [Equation 1], as the distance between zoom area 831 and zoom area 832 increases, the speed at which the movement speed of the target zoom area increases (e.g., the acceleration) may decrease. As a result, zoom area 833 can move stably (or smoothly) within multiple images 813.

[0176] In embodiments, when determining the parameter k related to the movement speed and size of the target zoom area, at least one element in [Equation 1] may be disregarded. For example, the speed (e.g., frames per second (FPS)) at which the plurality of images of the second video are acquired may be faster than the movement speed (e.g., maximum speed) of the second object in the second video by a specified speed or more. In this case, the parameter k related to the movement speed and size of the target zoom area may be determined by excluding μ*distance(B1, B2) from the elements in [Equation 1].

[0177] In an embodiment, the processor 120 may determine the target zoom area (eg, the position and size of the target zoom area) by using Equation 2 below based on a parameter k related to the moving speed and size of the target zoom area.

[0178] Equation 2

[0179] B n =k * B2+(1-k) * (B1)

[0180] B n] may represent the position and size of each target zoom area (e.g., zoom area 833 of image 813), B1 may represent the position and size of the first image 811, and B2 may represent the position and size of the second image 812. For example, the result of [Equation 2] may be as shown in [Equation 3] below.

[0181] Equation 3

[0182]

[0183] B l 、B t 、B r 、B b Can be expressed as B n The lengths of the left, top, right, and bottom sides of the . and It can represent the length of the left side, top side, right side and bottom side of B2 respectively. and It can represent the length of the left side, top side, right side and bottom side of B1 respectively.

[0184] In an embodiment, while acquiring the plurality of images 813 through the camera module 180 , the processor 120 may apply the position and size of each target zoom area to the plurality of images 813 .

[0185] Figure 8 An example of determining (e.g., predicting) the position and size of the object 822 in the second image 812 after the plurality of images 813 by using a specified method (e.g., a Kalman filter) is described. However, in an embodiment, the processor 120 may determine (e.g., predict) the position and size of the object 822 in the second image 812 by using the first video. Figure 11 and Figure 12 A method for determining (eg, predicting) the position and size of the object 822 in the second image 812 by using the first video is described in detail.

[0186] In an embodiment, the processor 120 may zoom in (or out) a target zoom area within the plurality of images 813, thereby acquiring a portion of the video. Figure 8 According to the method described above, the processor 120 may repeatedly obtain the target zoom area, thereby obtaining a video.

[0187] In an embodiment, the processor 120 may store the acquired video in the memory 130 .

[0188] In an embodiment, the processor 120 may display the acquired video through a display (e.g., display module 160). For example, the processor 120 may continuously display an enlarged image of the target zoom area in the acquired video through a display (e.g., display module 160) in real time.

[0189] Although not in Figure 4 , but in an embodiment, the processor 120 may configure a mode corresponding to the parameters related to the zoom area. For example, the processor 120 may store the parameters related to the zoom area for a plurality of videos (e.g., the first video) in the memory 130. In an embodiment, when the processor 120 stores the parameters related to the zoom area in the memory 130, the processor 120 may store an image (e.g., a thumbnail) representing (or identifying) the parameters related to the zoom area in the memory 130 together with the parameters.

[0190] In an embodiment, the processor 120 may configure a mode for each parameter associated with a zoom region. When the user of the electronic device 101 selects a desired mode, the processor 120 may apply the parameters associated with the zoom region corresponding to the selected mode to the video to be recorded. Thus, the user may apply parameters associated with the zoom region of an object corresponding to the subject to be recorded, whose movements are similar to the movements of the subject to be recorded (e.g., dancing movements), to the video to be recorded.

[0191] In an embodiment, the processor 120 may apply the parameters related to the zoom area to the object corresponding to the subject based on the image acquired by the camera module 180 while displaying a preview through the display (e.g., the display module 160), thereby allowing the user to recognize whether the parameters related to the desired zoom area have been obtained. Figure 16 Various embodiments related thereto are described in detail.

[0192] Although not in Figures 4 to 9 , but in an embodiment, the processor 120 may adjust the configuration of the camera module 180 based on parameters related to the zoom area (e.g., the camera module 180 (e.g., by controlling the lens assembly 210)) to obtain a video of the second object from the second video. For example, the processor 120 may determine whether to move the lens assembly 210 based on a threshold value related to movement of the zoom area. The processor 120 may determine the movement speed (e.g., average speed) of the lens assembly 210 based on the average movement speed of the zoom area. The processor 120 may control the lens assembly 210 to perform a zoom-in or zoom-out operation based on the fill area of ​​the zoom area.

[0193] Although not in Figures 4 to 9, but in an embodiment, the processor 120 may move the electronic device based on the parameters related to the zoom area. For example, in a case where the electronic device 101 (e.g., an electronic device implemented in the form of a robot) includes a driving unit (e.g., a motor) capable of moving the position of the electronic device 101 or changing the direction faced by the electronic device 101 (e.g., the camera module 180), the processor 120 may move the electronic device 101 by controlling the driving unit based on the parameters related to the zoom area. As another example, in a case where the electronic device 101 is mounted on an external electronic device (e.g., a gimbal) capable of moving the electronic device 101, the processor 120 may send a signal for moving the electronic device 101 to the external electronic device through the communication module 190 based on the parameters related to the zoom area.

[0194] Figure 9 is a flowchart 900 for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0195] Figure 10 is an exemplary diagram 1000 for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0196] Reference Figure 9 and Figure 10 In operation 901, according to an exemplary embodiment, the processor 120 may identify a 1-1 time point of the first video corresponding to a 2-1 time point of the second video (eg, a current time point of the second video). Figure 10 In the example, the processor 120 may identify the 1-1 time point (t1) of the first video corresponding to the 2-1 time point (t3) of the second video. The operation of identifying the 1-1 time point of the first video corresponding to the 2-1 time point of the second video may be understood as an operation of synchronizing the second video with the first video.

[0197] In an embodiment, the processor 120 may compare the position (or change in position) of the second object at the 2-1 time point in the second video with the position (or change in position) of the first object in the first video, thereby identifying the 1-1 time point corresponding to the 2-1 time point.

[0198] In an embodiment, the processor 120 may identify the position (or change in position) of the second object 1023 included in the image 1021 at the 2-1 time point of the second video. The processor 120 may identify the first object 1013 included in the image 1011 in the first video corresponding to the position of the second object 1023 at the 2-1 time point of the second video from a plurality of images in the first video. For example, the processor 120 may identify a change (e.g., displacement) in the position of the first object included in a specified number of images preceding the image 1021. The processor 120 may determine the 1-1 time point (t1) of the image 1011 including the second object, whose position has changed (e.g., moved) in the first video to correspond to the change in position of the first object (e.g., similar to the change in position of the first object). However, the present disclosure is not limited thereto, and the processor 120 may identify a time point based on the start time point of the first video (e.g., a time point after the first time as the start time point of the first video) corresponding to the 2-1 time point (t3) of the second video based on the start time point (e.g., capture time point) of the second video (e.g., a time point after the first time from the start time point (e.g., capture time point) of the second video). In a case where the position of the second object at the 2-1 time point corresponds to the position of the first object at the time point of the first video based on the start time point of the first video, the processor 120 may determine the time point based on the start time point of the first video as the 1-1 time point (t1).

[0199] In an embodiment, the processor 120 may identify the 1-1 time point corresponding to the 2-1 time point based on the time of the second video and the time of the first video. For example, the processor 120 may determine the time point based on the start time point of the first video (e.g., a time point after the first time as the start time point of the first video) corresponding to the 2-1 time point (t3) of the second video based on the start time point (e.g., capture time point) of the second video (e.g., a time point after the first time from the start time point (e.g., capture time point) of the second video as the 1-1 time point (t1).

[0200] In an embodiment, the processor 120 may identify the 1-1th time point corresponding to the 2-1th time point based on the audio signal.

[0201] In an embodiment, while acquiring the second video through the camera module 180, the processor 120 may acquire an audio signal (e.g., an audio signal related to music) through a microphone (e.g., the input module 150). The processor 120 may identify the waveform of the acquired audio signal and / or the energy of the audio signal.

[0202] In an embodiment, the processor 120 may identify a correlation between the acquired audio signal and the audio signal of the first video based on the waveform of the acquired audio signal and the waveform of the audio signal of the first video. The processor 120 may identify the waveform of the audio signal acquired for a specified time point before the 2-1 time point and a time segment of the first video having a high correlation (e.g., a correlation greater than or equal to a specified value). The processor 120 may determine the last time point of the time segment of the first video as the 1-1 time point (t1) corresponding to the 2-1 time point (t3).

[0203] In an embodiment, the processor 120 may identify the energy of the acquired audio signal and the energy of the audio signal of the first video as corresponding audio signals (for example, audio signals for the same music) based on the energy of the acquired audio signal and the energy of the audio signal of the first video. The processor 120 may identify a time segment of the first video in which the energy of the audio signal of the first video is acquired, and the energy has a difference of a specified value or less relative to the energy of the audio signal acquired in the time segment before the 2-1 time point. The processor 120 may determine the last time point of the time segment of the first video as the 1-1 time point (t1) corresponding to the 2-1 time point (t3).

[0204] In an embodiment, the processor 120 may identify the first object corresponding to the second object based on at least one of the time, beat, rhythm, and pace of the acquired audio signal. For example, the processor 120 may identify at least one of the time, beat, rhythm, and pace of the audio signal acquired at the 2-1 time point of the second video. The processor 120 may identify the time point of at least one of the time, beat, rhythm, or pace corresponding to at least one of the time, beat, rhythm, and pace of the audio signal acquired in the first video as the 1-1 time point of the first video corresponding to (or synchronized with) the 2-1 time point of the second video.

[0205] In an embodiment, the processor 120 may identify the first object corresponding to the second object based on the voice signal included in the acquired audio signal. For example, the processor 120 may identify the voice signal (e.g., the user's voice signal) included in the audio signal acquired at the 2-1 time point of the second video, for example, by using a voice recognition module. The processor 120 may identify the time point at which the voice signal corresponding to the voice signal included in the audio signal acquired in the first video is identified as the 1-1 time point of the first video corresponding to the 1 time point of the second video.

[0206] In an embodiment, the processor 120 may identify the first object corresponding to the second object based on the noise (e.g., ambient noise or white noise) included in the acquired audio signal. For example, the processor 120 may identify the noise included in the audio signal acquired at the 2-1 time point of the second video. The processor 120 may identify the time point at which the voice signal corresponding to the noise included in the audio signal acquired in the first video is identified as the 1-1 time point of the first video corresponding to the 1 time point of the second video.

[0207] In operation 903, according to an embodiment, the processor 120 may determine the position and size of the first object in the next image of the image at the 1-1 time point of the first video. Figure 10 , the processor 120 may determine the position and size of the first object 1014 of the image 1012 which is the next image (eg, the next image frame) of the image 1011 .

[0208] Operation 903 and Figure 10 It is illustrated that the position and size of the first object 1014 included in the image 1012 which is the next image (eg, the next image frame) of the image 1011 is determined, but is not limited thereto.

[0209] In an embodiment, the processor 120 may determine the position and size of the first object in the image at the 1-2 time point, which is a time point after the 1-1 time point (t1). In an embodiment, the processor 120 may determine the 1-2 time point of the first video taking into account the frame rate of the first video and the frame rate of the second video.

[0210] In operation 905, in an embodiment, the processor 120 may obtain, in the second video, a position and size of a predicted object in an image corresponding to a next image (e.g., image 1012) of the first video, corresponding to the position and size of the first object in the next image (e.g., image 1012).

[0211] For example, based on the position and size of the first object 1014 in the image 1012, the processor 120 may determine the position and size of the object 1024 to be included in the image 1022 of the second video at the 2-2 time point (t4) corresponding to the 1-2 time point (t2) of the first video (e.g., the position and size of the object 1024 predicted to be included in the image 1022 at the 2-2 time point (t4). The processor 120 may determine the 2-2 time point (t4) of the second video by adding the difference between the 1-2 time point (t2) and the 1-2 time point (t1) to the 2-1 time point (e.g., the current time point) of the second video.

[0212] In operation 907, in an embodiment, the processor 120 may determine a zoom area based on the determined position and size of the object. For example, the processor 120 may determine the determined zoom area of ​​the object 1024 (e.g., the determined position and size of the object 1024) by using parameters related to the zoom area (e.g., default parameters related to the zoom area).

[0213] In operation 909, according to an embodiment, the processor 120 may apply the determined zoom area to an image subsequent to the image acquired at time point 2-1 of the second video. For example, the processor 120 may apply the determined zoom area to a second object included in the next image of image 1021 of the second video (which is acquired by the camera module 180 after time point 2-1 (t3) (or stored in the memory 130 after being acquired by the camera module 180)).

[0214] In an embodiment, the processor 120 may acquire a video by repeatedly performing operations 901 to 909 .

[0215] In an embodiment, the processor 120 may acquire a video by repeatedly performing operations 903 to 909 except for operation 901 after performing operations 901 to 909. For example, when the frame rate of the first video and the frame rate of the second video are the same, the processor 120 may perform operation 901 to synchronize the time point of the first video and the time point of the second video, and therefore, there may be no need to perform operation 901 again in subsequent operations.

[0216] In the embodiment, although not in Figure 9 , but in the case where the 1-1 time point of the first video corresponding to the 2-1 time point of the second video (for example, the current time point of the second video) cannot be identified in operation 901, the processor 120 may Figure 7 or Figure 8 The method described is used to obtain a video. For example, after a time point of a first video and a time point of a second video are synchronized, a subject corresponding to a second object in the second video may move differently from a subject corresponding to the first object in the first video for a specified time (for example, in a case where the subject corresponding to the second object dances differently from a subject corresponding to the first object). In the case where a 1-1 time point of the first video corresponding to a 2-1 time point of the second video (for example, a current time point of the second video) cannot be identified for a specified time or a position (and size) of the second object in the second video is identified as changing differently from a position (and size) of the first object in the first video for a specified time, the processor 120 may generate a decoded image by referring to Figure 7 or Figure 8The described method applies default parameters related to the zoom area to the second video to obtain the video.

[0217] Figure 11 1100 is a flowchart for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0218] Figure 12 is an exemplary diagram 1200 for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0219] Reference Figure 11 and Figure 12 In an embodiment, in operation 1101, the processor 120 may identify a 1-1 time point of the first video corresponding to a 2-1 time point of the second video (eg, a current time point of the second video). Figure 12 , the processor 120 may identify a 1-1 time point (t1) of the first video that corresponds to a 2-1 time point (t3) of the second video.

[0220] Because the operation 1101 of identifying the 1-1 time point of the first video corresponding to the 2-1 time point of the second video is at least partially related to Figure 9 The operation is the same as or similar to operation 901, so its detailed description will be omitted.

[0221] According to an embodiment, in operation 1103, the processor 120 may determine the position and size of the first object included in the image at the 1-2 time point after the 1-1 time point of the first video. Figure 12 , the processor 120 may determine the position and size of the first object 1242 in the image 1212 including the 1-2 time point ( t2 ) which is the next time point after the 1-1 time point ( t1 ).

[0222] According to an embodiment, the 1-2 first time point t2 of the first video can be a time point considering the frame rate of the first video and the frame rate of the second video from the 1-1 time point (t1) of the first video (for example, the time point when image 1211 is acquired), the time required to detect the second object in each of the multiple images of the second video, and the time required to determine (for example, predict) the zoom area in operation 1109 to be described later.

[0223] In an embodiment, in operation 1105, the processor 120 may determine (e.g., predict) a position and size of an object corresponding to the position and size of the first object at time point 1-2 within an image at time point 2-1 of the second video corresponding to time point 1-2 of the first video.

[0224] In an embodiment, based on the position and size of the first object 1242 included in the image 1212 at the 1-2 time point of the first video, the processor 120 may determine the position and size of the object 1243, which is predicted to be included in the image 1222 at the 2-2 time point (t4) of the second video corresponding to the 1-2 time point of the first video. The processor 120 may determine the 2-2 time point (t4) of the second video by adding the difference between the 1-2 time point (t2) and the 1-2 time point (t1) to the 2-1 time point (e.g., the current time point) of the second video.

[0225] In an embodiment, the processor 120 may determine a zoom area corresponding to the determined position and size of the object in operation 1107. For example, the processor 120 may determine a zoom area 1233 (e.g., the position and size of the zoom area 1233) of the determined object 1243 (e.g., the determined position and size of the object 1243) by using parameters related to the zoom area (e.g., default parameters related to the zoom area).

[0226] In one embodiment, processor 120 may determine the size of zoom region 1233 at time point 2-2, taking into account the size of the first object included in a plurality of images acquired during a period from a time point before a first time point (t1) based on time point 1-1 of the first video to a time point after a second time point (t1) based on time point 1-1 of the first video. For example, processor 120 may determine (e.g., adjust) the size of zoom region 1233 at time point 2-2 by applying a padding region with default parameters to the region of the first object (e.g., the region including the first object) of the largest size among the sizes of the first objects included in the plurality of images (or by matching (mapping) the region of the first object of the largest size to the second video based on the ratio between the regions of the first and second videos of the object). In this case, the size of the target zoom region, which will be described later in operation 1109, may be determined to be larger (or wider) than the size of the target zoom region obtained by applying only the padding region with default parameters, and a video with less shaky motion may be obtained from the second video.

[0227] In an embodiment, in operation 1109, the processor 120 may determine a zoom area to be applied to multiple images to be acquired between the 2-1 time point and the 2-2 time point based on the determined zoom area and the zoom area of ​​the image at the 2-1 time point of the second video.

[0228] In an embodiment, the processor 120 may determine a zoom area 1231 of the second object 1241 included in the image 1221 at the 2-1st time point. The processor 120 may acquire parameters related to multiple zoom areas (hereinafter, referred to as "target zoom areas") to be applied to multiple images (e.g., image 1223) to be acquired between the 2-1st time point and the 2-2nd time point based on the zoom area 1231 and the determined zoom area 1233 at the 2-2nd time point.

[0229] In an embodiment, the processor 120 may use a reference Figure 8 The parameters related to the moving speed and size of the target zoom area are determined by using the equation 1 described above. For example, when applying [Equation 1] to Figure 12 In the case of the embodiment of FIG. 1 , in [Equation 1], B1 may represent the position and size of the zoom area 1231 of the image 1221, and B2 may represent the position and size of the zoom area 1233. Figure 12 [Equation 1] of the embodiment, in order to avoid Figure 8 The description of is overlapped, and its detailed description will be omitted.

[0230] In an embodiment, the processor 120 may use a reference Figure 8 The target zoom area (e.g., the position and size of the target zoom area) is determined by using the [Equation 2] described above. Figure 12 In the case of the embodiment of FIG, in [Equation 2], B1 may represent the position and size of the zoom area 1231 of the image 1221, and B2 may represent the position and size of the zoom area 1233 of the image 1223. Figure 12 [Equation 2] of the embodiment, in order to avoid Figure 8 The description of is overlapped, and its detailed description will be omitted.

[0231] In an embodiment, in operation 1111, the processor 120 may acquire a third video from the second video based on a zoom area (e.g., a target zoom area). For example, while acquiring multiple images (e.g., image 1223) through the camera module 180, the processor 120 may acquire the third video by applying the position and size of each target zoom area (e.g., zoom area 1235) to the multiple images. For example, the processor 120 may acquire the third video by cropping each of the multiple images included in the second video based on the position and size of the target zoom area.

[0232] Figure 11The description of determining the position and size of an object corresponding to the position and size of the first object at time point 1-1 in the image at time point 2-1 of the second video and then determining a zoom area corresponding to the determined position and size of the object has been described, but the present invention is not limited thereto. For example, processor 120 may also determine the zoom area of ​​the first object based on the position and size of the first object at time point 1-2 of the first video, and then determine a zoom area at time point 2-2 of the second video corresponding to the determined zoom area of ​​the first object.

[0233] Figure 13 1300 is a flowchart for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0234] Figure 14 is an exemplary diagram 1400 for explaining a method for applying parameters related to a zoom area to be applied to a second video by using a first video according to various embodiments.

[0235] Reference Figure 13 and Figure 14 In an embodiment, in operation 1301, the processor 120 may identify a 1-1 time point of the first video corresponding to a 2-1 time point of the second video (eg, the current time point of the second video). Figure 14 , the processor 120 may identify a 1-1 time point (t1) of the first video that corresponds to a 2-1 time point (t3) of the second video.

[0236] Because the operation 1301 of identifying the 1-1 time point of the first video corresponding to the 2-1 time point of the second video is at least partially related to Figure 9 Operation 901 and Figure 12 The operation 1201 is the same as or similar to that of FIG. 1201 , so a detailed description thereof will be omitted.

[0237] According to an embodiment, in operation 1303, the processor 120 may determine the position and size of the first object included in the image at the 1-2 time point after the 1-1 time point of the first video. Figure 14 , the processor 120 may determine a position and a size of a first object 1442 included in an image 1412 at a 1-2 time point (t2) which is a time point after a 1-1 time point (t1) (eg, a time point at which the image 1411 is acquired).

[0238] In an embodiment, in operation 1305, the processor 120 may determine (e.g., predict) a position and size of an object corresponding to the position and size of the first object at time point 1-2 within an image at time point 2-1 of the second video corresponding to time point 1-2 of the first video.

[0239] In an embodiment, the processor 120 may determine the position and size of the object 1443, which is predicted to be included in the image 1422 of the second video at the 2-2 time point (t4) corresponding to the 1-2 time point of the first video, based on the position and size of the first object 1442 included in the image 1412 at the 1-2 time point of the first video. The processor 120 may determine the 2-2 time point (t4) by adding the difference between the 1-2 time point (t2) and the 1-2 time point (t1) to the 2-1 time point (e.g., the current time point) of the second video.

[0240] In an embodiment, in operation 1107 , the processor 120 may determine parameters related to the zoom area based on a difference between the position and size of the second object at the 2-1 time point and the determined position and size of the object.

[0241] In an embodiment, the processor 120 may determine the position and size of the second object 1441 included in the image 1421 at the 2-1 time point of the second video. The processor 120 may adjust the movement speed of the zoom area of ​​the parameters related to the zoom area (e.g., default parameters) so that the greater the difference between the position of the second object 1441 and the position of the object 1443, the greater the movement speed of the zoom area (e.g., faster). The processor 120 may adjust the movement speed of the zoom area of ​​the parameters related to the zoom area (e.g., default parameters) so that the smaller the difference between the position of the second object 1441 and the position of the object 1443, the smaller the movement speed of the zoom area (e.g., slower). The processor 120 may consider the size of the second object 1441 and the size of the object 1443 when adjusting the movement speed of the zoom area of ​​the parameters related to the zoom area (e.g., default parameters).

[0242] In an embodiment, in operation 1309 , the processor 120 may acquire a third video from the second video based on parameters related to the zoom area.

[0243] In an embodiment, the processor 120 may apply the parameters related to the zoom area determined in operation 1307 with respect to an image to be acquired from the 2-1st time point ( t3 ) to the 2-2nd time point ( t4 ).

[0244] In an embodiment, the processor 120 may apply the parameters related to the zoom area adjusted by the default parameters to the image to be acquired from the 2-1 time point (t3) to the (2-2) time point (t4), thereby acquiring the third video from the second video.

[0245] Figure 15 is a flowchart 1500 illustrating a method for obtaining a first video by selecting the first video from a plurality of videos according to various embodiments.

[0246] According to Figure 15 In an embodiment, in operation 1501, the processor 120 may identify whether an input for automatically selecting a first video (e.g., in a specified manner) is received. The input for automatically selecting the first video may include, for example, an input through a user interface (e.g., a check box) for receiving user input for automatically selecting the first video or setting information (e.g., flag type information) for automatically selecting the first video. For example, the processor 120 may receive user input through a user interface included in a camera application before starting to capture an image or receive user information through preconfigured setting information.

[0247] In an embodiment, in a case where an input for automatically selecting a first video is received in operation 1501 , the processor 120 may analyze a video acquired (eg, being acquired) through the camera module 180 in operation 1503 .

[0248] In an embodiment, upon receiving an input for automatically selecting a first video, the processor 120 may display a preview through a display (e.g., the display module 160) based on a plurality of images acquired through the camera module 180. In an embodiment, the processor 120 may analyze a screen composition and / or background of the preview acquired through the camera module 180. In an embodiment, the processor 120 may analyze at least one object included in the preview acquired through the camera module 180.

[0249] In an embodiment, in operation 1505, the processor 120 may select a video corresponding to the video analyzed in operation 1503 from the plurality of videos stored in the memory 130 as the first video. For example, the processor 120 may analyze the background and / or screen composition of the images within a specified time period from the starting point for each of the plurality of videos stored in the memory 130. The processor 120 may select, as the first video, an image having a background and / or screen composition similar to that of the video (e.g., preview) analyzed in operation 1503 from the plurality of videos stored in the memory 130. As another example, the processor 120 may analyze the objects included in the images within a specified time period from the starting point for each of the plurality of videos stored in the memory 130. The processor 120 may select, as the first video, a video including an object similar to that of the video (e.g., preview) analyzed in operation 1503 from the plurality of videos stored in the memory 130. However, the method for automatically selecting the first video from the plurality of videos stored in the memory 130 is not limited to the above example.

[0250] In an embodiment, if no input for automatically selecting a first video is received in operation 1501, the processor 120 may select a video from a plurality of videos as the first video based on user input in operation 1507. For example, the processor 120 may receive user input for selecting a video from a plurality of videos. The processor 120 may determine the selected video as the first video based on the user input.

[0251] Figure 16 is a flow chart 1600 illustrating a method for applying parameters related to a zoom area with respect to an object corresponding to a subject while displaying a preview, according to various embodiments.

[0252] In one embodiment, Figure 16 A method may be involved, comprising: after obtaining parameters (e.g., default parameters) related to a zoom region (e.g., after executing Figure 4 After operation 403, the parameters related to the acquired zoom area are applied to the preview so that the user can recognize whether the parameters related to the zoom area desired by the user are acquired.

[0253] Reference Figure 16 In an embodiment, in operation 1601, the processor 120 may determine parameters related to a zoom area (eg, default parameters).

[0254] In an embodiment, at operation 1603 , the processor 120 may display an indication indicating a zoom area while displaying a preview through a display (eg, the display module 160 ) based on a plurality of images acquired through the camera module 180 .

[0255] In an embodiment, when an object is detected while displaying the preview, the processor 120 may apply default parameters to the detected object. While applying the default parameters to the detected object, the processor 120 may display an indication of a boundary (e.g., four rectangular lines if the zoom area has a rectangular shape).

[0256] In an embodiment, the user can identify whether the parameters related to the zoom area desired by the user have been obtained by the movement and size change of the indication of the boundary of the zoom area in the preview indicating the application of default parameters. In an embodiment, if the parameters related to the zoom area desired by the user have not been obtained, the processor 120 can obtain the parameters related to the zoom area desired by the user by adjusting the parameters related to the zoom area based on user input.

[0257] In an embodiment, the processor 120 may configure the display of the preview video and / or the indication of the zoom area differently based on the state of the electronic device 101. For example, the electronic device 101 may be a foldable electronic device or an expandable (or scrollable, slidable) electronic device. For example, in the case where the electronic device 101 is a foldable electronic device, the screen display area may be reduced in the folded state of the electronic device 101. The processor 120 may detect the state of the electronic device 101 by at least one sensor (e.g., a Hall sensor), and may adjust the size of the preview video based on the size of the screen display area when the electronic device 101 is folded. The processor 120 may display an indication of the zoom area on the adjusted preview screen. As another example, in the case where the electronic device 101 is folded, the processor 120 may adjust (e.g., crop) the preview video based on the zoom area. For example, the processor 120 may adjust the preview video so that the zoom area indication is included in the preview video to the greatest extent. For example, processor 120 can adjust the preview video so that the space (e.g., 10px) at the top / bottom or left / right of the area other than the zoom area of ​​the specified size is included in the preview video. In this case, the zoom area can be displayed in the center area of ​​the display area.

[0258] According to various embodiments of the present disclosure, a method for providing a video in an electronic device 101 may include acquiring a first video, identifying a first object in the first video, acquiring parameters related to a zoom area based on movement of the first object in the first video, identifying a first object corresponding to a second object included in a second video acquired by a camera module 180 of the electronic device 101, and acquiring a video of the second object from the second video based on the parameters related to the zoom area.

[0259] In various embodiments, acquiring the video may include determining a zoom region associated with the second object from the second video based on parameters associated with the zoom region.

[0260] In various embodiments, the parameter related to the zoom area may include at least one of a threshold related to movement of the zoom area, an average movement speed of the zoom area, or a fill area of ​​the zoom area.

[0261] In various embodiments, acquiring the video may include: determining a size of the zoom region based on the fill region; and moving the zoom region based on an average movement speed if the second object movement threshold is greater than or equal to the second object movement threshold.

[0262] In various embodiments, identifying the first object may include identifying an object at a location corresponding to the location of the second object as the first object in the first video.

[0263] In various embodiments, identifying the first object may include identifying a first time in the second video corresponding to the second object, and identifying the first object corresponding to a second time of the first video based on the identified first time.

[0264] In various embodiments, identifying the first object may include acquiring an audio signal through a microphone of the electronic device 101 (e.g., input module 150), identifying a second time corresponding to the first time based on the audio signal and an audio signal included in the first video, and identifying an object included in the first video at the second time as the first object.

[0265] In various embodiments, identifying a second time corresponding to a first time may include recognizing at least one of a time, a beat, a rhythm, or a pace of the acquired audio signal, and identifying the second time corresponding to the first time based on at least one of the recognized time, a beat, a rhythm, or a pace.

[0266] In various embodiments, identifying the second time corresponding to the first time may include recognizing a voice signal included in the acquired audio signal, and identifying the second time corresponding to the first time based on the recognized voice signal.

[0267] In various embodiments, identifying the first object in the first video may further include: identifying whether the first video includes multiple objects; and if the first video includes the multiple objects, identifying a first object corresponding to a second object among the multiple objects.

[0268] In various embodiments, acquiring a video may include identifying a second time point of the first video corresponding to a first time point of the second video, determining a position and size of a first object in the first video at a third time point after the second time point of the first video, determining a position and size of a zoom area based on the determined position and size of the first object and parameters related to the zoom area, and acquiring a video related to the second object from the second video based on the determined position and size of the zoom area.

[0269] In various embodiments, the method may further include determining a size of a first object included in each of a plurality of images of the first video, the plurality of images having been acquired within a time from a time point before the first time relative to a second time point of the first video to a time point after the second time relative to the second time point, wherein determining the position and size of the zoom area may include acquiring a video related to the second object from the second video based on the determined position and size of the zoom area.

[0270] In various embodiments, identifying a second time point of the first video corresponding to the first time point of the second video may include identifying the second time point corresponding to the first time point based on the position of the second object and the position of the first object or at least one of an audio signal obtained by a microphone of the electronic device 101 and an audio signal included in the first video.

[0271] In various embodiments, the method may include adjusting settings of the camera module 180 based on parameters related to the zoom area, and controlling the camera module 180 based on the adjusted settings of the camera module 180 to acquire video of the second object.

[0272] In addition, the structure of the data used in the above-mentioned embodiments of the present disclosure can be recorded in a computer-readable recording medium by various means. Computer-readable recording media include storage media such as magnetic storage media (e.g., ROM, floppy disk, hard disk, etc.) and optically readable media (e.g., CD-ROM, DVD, etc.).

[0273] The computer-readable recording medium recording a computer executable program may record a program for executing: acquiring a first video in the electronic device 101, identifying a first object in the first video, acquiring parameters related to a zoom area based on movement of the first object in the first video, identifying a first object corresponding to a second object included in a second video acquired by the camera module 180 of the electronic device 101, and acquiring a video of the second object from the second video based on the parameters related to the zoom area.

[0274] The present disclosure has been described with focus on the embodiments thereof. Those skilled in the art will appreciate that the present disclosure may be implemented in a modified form without departing from the essential features of the present disclosure. Therefore, the disclosed embodiments are to be considered illustrative rather than restrictive. The scope of the present disclosure is indicated in the claims rather than in the foregoing description, and all differences within the scope of equivalence thereto are to be construed as included in the present disclosure.

[0275] Sequence Listing Independent Text

[0276] 101: Electronic device 120: Processor

[0277] 130: Memory 160: Display module

[0278] 180: Camera module 195: Communication circuit

Claims

1. An electronic device comprising: Camera module; a processor functionally connected to the camera module; as well as A memory storing instructions and a first video, wherein the instructions, when executed by a processor, cause the electronic device to: Get the first video from the memory, identifying a first object in a first video, acquiring parameters related to a zoom area based on movement of a first object in the first video, the parameters to be applied to a second object, the second object corresponding to the first object and included in a second video acquired by the camera module, the first object and the second object representing the same person; After obtaining the parameters related to the zoom area, obtaining a second video through the camera module; When acquiring a second video through a camera module, identifying a first object corresponding to a second object included in the second video, When acquiring a second video through the camera module, determining a zoom area of ​​the second object in the second video by applying the parameters related to the zoom area to the second object, where the zoom area of ​​the second object is an area to be cropped in the second video, and the zoom area of ​​the second object includes a tracking area surrounding the second object and a filling area surrounding the tracking area; as well as obtaining a third video of the second object from the second video by cropping the zoomed area of ​​the second object in the second video, The parameters related to the zoom area include at least one of a threshold for determining whether to move the zoom area in the second video, a moving speed of the zoom area in the second video, and a size of a fill area of ​​the zoom area.

2. The electronic device according to claim 1, wherein the instruction causes the electronic device to: determining the threshold based on a size of an area in the first video where the first object has moved; in, moving the zoom region of the second object based on the second object moving by a threshold value or more in the second video; Wherein, based on the second object moving by an amount less than a threshold, the zoom area of ​​the second object is not moved.

3. The electronic device according to claim 1, wherein the instruction causes the electronic device to: determining an average moving speed of the first object in the first video as a moving speed of the zoom area in the second video, and The size of the fill area of ​​the zoom area is determined based on a change in position and size of the first object in the first video. 4 . The electronic device of claim 1 , wherein the instructions cause the electronic device to identify an object at a position in the first video corresponding to a position of the second object in the second video as the first object.

5. The electronic device of claim 1 , wherein the instruction causes the electronic device to: identifying a first time corresponding to a second object in a second video, and A first object corresponding to a second time of the first video is identified based on the identified first time.

6. The electronic device according to claim 5, further comprising a microphone, The instructions cause the electronic device to: Get the audio signal through the microphone, identifying a second time corresponding to the first time based on the acquired audio signal and an audio signal included in the first video, and An object included in the first video at the second time is identified as a first object.

7. The electronic device of claim 1 , wherein the instruction causes the electronic device to: identifying whether the first video includes a plurality of objects, and When the first video includes the plurality of objects, a first object corresponding to a second object is identified among the plurality of objects.

8. The electronic device of claim 1 , wherein the instruction causes the electronic device to: identifying a second time point of the first video corresponding to the first time point of the second video, determining a position and a size of a first object included in the first video at a third time point after the second time point in the first video, determining a position and size of the zoom area based on the determined position and size of the first object and parameters related to the zoom area, and A third video of the second object is obtained from the second video by cropping the zoom region having the determined position and size in the second video.

9. A method for providing a video in an electronic device, the method comprising: Acquire a first video stored in a memory of the electronic device; identifying a first object in a first video; acquiring parameters related to a zoom area based on movement of a first object in the first video, the parameters to be applied to a second object, the second object corresponding to the first object and included in a second video acquired by the camera module, the first object and the second object representing the same person; After obtaining the parameters related to the zoom area, obtaining a second video through the camera module; When acquiring a second video through a camera module, identifying a first object corresponding to a second object included in the second video, When acquiring a second video through the camera module, determining a zoom area of ​​the second object in the second video by applying the parameters related to the zoom area to the second object, where the zoom area of ​​the second object is an area to be cropped in the second video, and the zoom area of ​​the second object includes a tracking area surrounding the second object and a filling area surrounding the tracking area; as well as obtaining a third video of the second object from the second video by cropping the zoomed area of ​​the second object in the second video, The parameters related to the zoom area include at least one of a threshold for determining whether to move the zoom area in the second video, a moving speed of the zoom area in the second video, and a size of a fill area of ​​the zoom area.

10. The method according to claim 9, wherein obtaining the parameter comprises determining the threshold based on a size of an area in which the first object has moved in the first video, in, moving the zoom region of the second object based on the second object moving by a threshold value or more in the second video; Wherein, based on the second object moving by an amount less than a threshold, the zoom area of ​​the second object is not moved.

11. The method according to claim 9, wherein obtaining the parameter comprises: determining an average moving speed of the first object in the first video as a moving speed of the zoom area in the second video, and The size of the fill area of ​​the zoom area is determined based on a change in position and size of the first object in the first video. 12 . The method of claim 9 , wherein identifying the first object comprises identifying an object at a location in the first video corresponding to a location of the second object in the second video as the first object.

13. The method of claim 9, wherein identifying the first object comprises: identifying a first time corresponding to a second object in the second video; as well as A first object corresponding to a second time of the first video is identified based on the identified first time.

14. The method of claim 13, wherein identifying the first object comprises: acquiring an audio signal through a microphone of the electronic device; identifying a second time corresponding to the first time based on the acquired audio signal and an audio signal included in the first video; as well as An object included in the first video at the second time is identified as a first object.

15. A computer-readable medium having computer-executable instructions recorded thereon, the computer-executable instructions being configured to, when executed by a processor of an electronic device, cause the electronic device to perform the following operations: Acquire a first video stored in a memory of the electronic device; identifying a first object in a first video; acquiring parameters related to a zoom area based on movement of a first object in the first video, the parameters to be applied to a second object, the second object corresponding to the first object and included in a second video acquired by the camera module, the first object and the second object representing the same person; After obtaining the parameters related to the zoom area, obtaining a second video through the camera module; When acquiring a second video through a camera module, identifying a first object corresponding to a second object included in the second video, When acquiring a second video through the camera module, determining a zoom area of ​​the second object in the second video by applying the parameters related to the zoom area to the second object, where the zoom area of ​​the second object is an area to be cropped in the second video, and the zoom area of ​​the second object includes a tracking area surrounding the second object and a filling area surrounding the tracking area; as well as obtaining a third video of the second object from the second video by cropping the zoomed area of ​​the second object in the second video, The parameters related to the zoom area include at least one of a threshold for determining whether to move the zoom area in the second video, a moving speed of the zoom area in the second video, and a size of a fill area of ​​the zoom area.

Images