Video retrieval method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a video retrieval method and device, electronic equipment and storage medium, and relates to the technical field of artificial intelligence. The method comprises: extracting a target feature of a to-be-retrieved object; based on the target feature, retrieving video features stored in a to-be-retrieved video to obtain a target video corresponding to the to-be-retrieved object. The embodiment of the present disclosure can simplify the video retrieval process, greatly improve the retrieval efficiency and reduce the labor cost by using the video features of the to-be-retrieved video as labels and accurately positioning the target video through the labels.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a video retrieval method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of the times, surveillance has become an indispensable and important component in crime prevention, accident evidence collection, and alarm linkage. For example, home surveillance can be used to care for the elderly and children at home, while community surveillance can maintain public order and provide daily security for residents. This has led to the challenges of storing, processing, and applying massive amounts of surveillance video. How to quickly and accurately locate the required video clips from surveillance footage has become a pressing concern.

[0003] In related technologies, surveillance videos are often packaged into video file formats such as AVI and MP4 and stored on storage devices such as SD cards, TF cards, or hard drives. Surveillance can only collect video and audio, and cannot extract more multi-dimensional feature information, thus failing to provide diverse and intelligent retrieval services. To retrieve useful segments from surveillance videos, or to find target individuals or vehicles, it is necessary to manually select the time range of the surveillance video and review it frame by frame, resulting in long retrieval times, low accuracy, and high labor costs. Summary of the Invention

[0004] In view of this, this disclosure relates to the field of artificial intelligence technology, and more specifically, to a video retrieval method, apparatus, computer device, and storage medium.

[0005] Firstly, a video retrieval method is provided, comprising: extracting target features of the object to be retrieved; and retrieving video features stored in the video to be retrieved based on the target features to obtain the target video corresponding to the object to be retrieved.

[0006] In some embodiments, video features are stored in extended information of the video to be retrieved.

[0007] In some embodiments, the method further includes: extracting video features from the video to be retrieved; and storing the video features in the form of structured data in the extended information of the video to be retrieved.

[0008] In some embodiments, based on target features, a search is performed on the video features stored in the video to be searched to obtain a target video corresponding to the object to be searched, including: traversing the video features stored in the video to be searched; if the video features match the target features, then extracting at least one video segment corresponding to the video features in the video to be searched; and generating a target video based on at least one video segment.

[0009] In some embodiments, extracting at least one video segment corresponding to video features in a video to be retrieved includes: retrieving video frames of the video to be retrieved based on video features that match the target features; extracting video frames if they contain video features; and concatenating the extracted video frames in the order they appear in the video to be retrieved to generate at least one video segment.

[0010] In some embodiments, generating a target video based on at least one video segment includes: sequentially concatenating at least one video segment according to the order in which the at least one video segment is arranged in the video to be retrieved, thereby generating the target video.

[0011] In some embodiments, extracting target features of the object to be retrieved includes: obtaining the object to be retrieved, which includes at least one of text to be retrieved and image to be retrieved; extracting text feature information from the text to be retrieved, and / or extracting image feature information from the image to be retrieved; the target features include at least one of text feature information and image feature information.

[0012] Secondly, a video retrieval device is provided, comprising: an extraction module for extracting target features of an object to be retrieved; and a retrieval module for retrieving video features stored in the video to be retrieved based on the target features, thereby obtaining a target video corresponding to the object to be retrieved.

[0013] Thirdly, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of the first aspect by executing the executable instructions.

[0014] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of the first aspect described above.

[0015] The video retrieval method provided in this disclosure extracts target features of the object to be retrieved; based on the target features, it retrieves the video features stored in the video to be retrieved, thereby obtaining the target video corresponding to the object to be retrieved. This disclosure simplifies the video retrieval process and reduces labor costs by using the video features of the video to be retrieved as tags to accurately locate the target video. Attached Figure Description

[0016] Figure 1 A schematic diagram of the system architecture of a video retrieval method according to an embodiment of this disclosure is shown.

[0017] Figure 2 The diagram shows a flowchart of a video retrieval method according to an embodiment of this disclosure.

[0018] Figure 3This diagram illustrates a flowchart of the process for extracting target features of an object to be retrieved in an embodiment of this disclosure.

[0019] Figure 4 This illustration shows a process for generating a video to be retrieved in an embodiment of this disclosure.

[0020] Figure 5 A flowchart illustrating the target video acquisition method in an embodiment of this disclosure is shown.

[0021] Figure 6 This invention illustrates a user interaction method for a video retrieval method according to an embodiment of the present disclosure.

[0022] Figure 7 A schematic diagram of the structure of a video retrieval device according to an embodiment of the present disclosure is shown.

[0023] Figure 8 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0024] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0025] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] With the gradual advancement of public security video surveillance construction in my country, public area monitoring has achieved comprehensive grid-based and closed-loop management, fundamentally improving social security and further enhancing public safety and satisfaction. However, this has also resulted in a massive amount of surveillance video. While this video can comprehensively and accurately record the events or the movements of targets, finding the necessary video clips from this vast amount of irrelevant footage has become a pressing problem that needs to be solved.

[0027] In related technologies, video surveillance captures video and audio from a specific area and stores it on storage devices such as SD cards and hard drives, or on servers. To retrieve useful clips from the surveillance video, or to locate a target person or vehicle, it is necessary to manually select the time range of the surveillance video and review it frame by frame, which is labor-intensive.

[0028] In view of this, this disclosure provides a video retrieval method that extracts target features of the object to be retrieved; based on the target features, it retrieves video features stored in the video to be retrieved to obtain the target video corresponding to the object to be retrieved. Therefore, embodiments of this disclosure can use the video features of the video to be retrieved as tags to accurately locate the target video, thereby simplifying the video retrieval process and reducing labor costs.

[0029] This disclosure provides a video retrieval method, apparatus, electronic device, and storage medium. Specifically, the video retrieval apparatus can be integrated into an electronic device, which may be a terminal or server, etc.

[0030] It is understood that the video retrieval method of this embodiment can be executed on a terminal, on a server, or jointly by a terminal and a server. The above examples should not be construed as limiting this disclosure.

[0031] Figure 1 An exemplary system architecture diagram is shown that can be applied to the video retrieval method or video retrieval device in the embodiments of this disclosure.

[0032] like Figure 1 As shown, the system architecture 100 includes a terminal 101 and a server 102. The terminal 101 and the server 102 are connected via a network, such as a wired or wireless network, wherein the video retrieval device can be integrated into the server.

[0033] Server 102 can be used to extract target features of the object to be retrieved; based on the target features, the video features stored in the video to be retrieved are searched to obtain the target video corresponding to the object to be retrieved.

[0034] Server 102 can be a single server, a server cluster composed of multiple servers, or a cloud server. For example, a server can be an interoperability server between multiple heterogeneous systems, a backend server, an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, and big data and artificial intelligence platforms, etc. As in the speech synthesis method or apparatus disclosed in this disclosure, multiple servers can form a blockchain, and the server is a node on the blockchain.

[0035] Terminal 101 can send the object to be retrieved to server 102, or receive the target video generated by server 102. Terminal 101 may include a mobile phone, smart TV, tablet computer, laptop computer, or personal computer (PC), etc. A client may also be configured on terminal 101, which may be an application client or a browser client, etc.

[0036] Those skilled in the art will know that Figure 1 The number of terminals and servers shown is merely illustrative. Depending on actual needs, there may be any number of terminals and servers, and this disclosure does not impose any limitation on this.

[0037] The following will describe the exemplary implementation method in detail with reference to the accompanying drawings and embodiments.

[0038] First, this disclosure provides a video retrieval method, which can be executed by any electronic device with computing power.

[0039] Figure 2 This diagram illustrates a flow chart of a video retrieval method according to an embodiment of the present disclosure, as shown below. Figure 2 As shown, the video retrieval method provided in this embodiment includes the following steps.

[0040] S201, Extract the target features of the object to be retrieved.

[0041] Specifically, the target features of the object to be retrieved can be used to distinguish different retrieval targets. For example, retrieval targets could be vehicles, people, and geographical locations.

[0042] In some embodiments, such as Figure 3 As shown, extracting the target features of the object to be retrieved includes the following steps.

[0043] S2011, Obtain the object to be retrieved, which includes at least one of the text to be retrieved and the image to be retrieved.

[0044] In some embodiments, the object to be searched refers to text or image to be searched. For example, the text to be searched includes the license plate number, brand or color of a vehicle, the gender, age range or height range of a person, road sign information of a geographical location, and shop name; the image to be searched includes a photo of a face or a photo of a location, or a street view photo of the target location.

[0045] S2012, extract text feature information from the text to be retrieved, and / or, extract image feature information from the image to be retrieved.

[0046] Specifically, text recognition algorithms can be used to extract textual features from the text to be retrieved, and image recognition algorithms can be used to extract image features from the image to be retrieved. The target features can be textual features or image features alone, or they can include both types of features simultaneously.

[0047] S202, Based on the target features, retrieve the video features stored in the video to be retrieved to obtain the target video corresponding to the object to be retrieved.

[0048] Specifically, the video to be retrieved is a pre-recorded, processed, and stored video. Based on target features, stored video features are retrieved from the video to determine if it contains the object to be retrieved. If so, the video segment containing the object to be retrieved is extracted from the video to be retrieved and used as the target video.

[0049] In some embodiments, video features are characteristics of people, vehicles, or objects in the video. For example, these may include vehicle features, person features, or geographic location features. Examples include license plate numbers, facial feature matrices, or GPS coordinates obtained using GPS sensors or the Internet of Things (IoT).

[0050] In some embodiments, video features are stored in extended information of the video to be retrieved. For example, the extended information may be SVAC extended information, where each frame of the video can carry different SVAC extended information, thus allowing for frame-by-frame retrieval of videos using SVAC extended information.

[0051] In some embodiments, video features can be extracted synchronously by an artificial intelligence model as video features during video recording, and stored in real time in the extended information of the video to be retrieved in the form of structured data.

[0052] Figure 4 The diagram illustrates the process of generating the video to be retrieved in an embodiment of this disclosure. For example... Figure 4As shown, the camera captures video of a specific area; the video is input into an embedded neural network processor (NPU) to perform high-speed, parallel matrix operations in real time, extracting video features from the video, writing the video features into the extended information of the video; and the video carrying the video features is stored in a storage device such as a hard drive or a server.

[0053] For example, Figure 5 A flowchart illustrating the target video acquisition method in an embodiment of this disclosure is shown. Figure 5 As shown, the method includes the following steps.

[0054] S2021, Traverse the video features stored in the video to be retrieved.

[0055] Specifically, based on the target features, the system iterates through the stored video features in the video to be retrieved, finding video features that match the target features. For example, string search, matrix distance operations, and matrix similarity calculations can be used to match the target features with the video features.

[0056] In some embodiments, users can first determine the approximate search time period based on the search target, and then determine the videos to be searched based on the search time period to narrow down the search scope.

[0057] S2022, If the video features match the target features, then extract at least one video segment corresponding to the video features in the video to be retrieved.

[0058] In some embodiments, each video frame in the video to be retrieved can be retrieved based on video features matching the target features; if the video frame contains one or more video features matching the target features, then the video frame is extracted; all extracted video frames are arranged sequentially in their original order. For example, the order of video frames can be determined according to the timestamp order corresponding to the video frames. If two video frames are adjacent in the video to be retrieved, then the two video frames are concatenated; if they are not adjacent in the video to be retrieved, then the two video frames are not concatenated. In this way, one or more video segments can be generated.

[0059] S2023, Generate a target video based on at least one video segment.

[0060] In some embodiments, all extracted video segments are arranged sequentially according to the order in the video to be retrieved and then concatenated in pairs to generate a target video. This target video includes all video segments from the appearance to the disappearance of the target.

[0061] In some embodiments, users can interact with an application through a user terminal to retrieve the video to be searched and obtain the target video. For example... Figure 6 As shown, the user interaction method of the video retrieval method includes the following steps.

[0062] S601, send parameter information.

[0063] Specifically, users input parameter information through their user terminals, which can be a string indicating the search target, a face photo, or a location photo.

[0064] S602, Extract target features.

[0065] Specifically, the feature extractor uses artificial intelligence algorithms to extract target features from parameter information. For example, recognition algorithms can be used to identify license plate numbers, vehicle colors, and age information from strings; facial recognition algorithms can be used to extract facial feature matrices from facial photos; and location recognition algorithms can be used to extract image feature matrices from location photos, etc.

[0066] S603, retrieve the video to be retrieved based on target features.

[0067] Specifically, the video retrieval method provided in this disclosure can be used to retrieve videos to be retrieved from a video library based on target features, thereby obtaining the target video corresponding to the parameter information. For example, the video library may be a storage device or a server.

[0068] S604, Return to target video.

[0069] Specifically, the video library sends the retrieved target videos to the user's terminal.

[0070] This disclosed embodiment can extract feature information carried by each frame of a video in real time while the video is being captured, and store it in the video's extended information in the form of structured data. This helps to enrich the multi-dimensional and highly abstract feature information of the video, providing data support for improving the speed and accuracy of video retrieval. When retrieving a target video, the video features in the extended information of the video to be retrieved are retrieved through the target features of the object to be retrieved, and the required target video is accurately located using the video features as tags. Therefore, it can avoid manually checking the video frame by frame, reducing labor costs.

[0071] Based on the same inventive concept, this disclosure also provides a video retrieval device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the method embodiments described above, the implementation of this device embodiment can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0072] Figure 7 This diagram illustrates the structure of a video retrieval device according to an embodiment of the present disclosure, such as... Figure 7 As shown, the video retrieval device 700 includes an extraction module 701 and a retrieval module 702.

[0073] Specifically, the extraction module 701 is used to extract the target features of the object to be retrieved. The retrieval module 702 is used to retrieve the video features stored in the video to be retrieved based on the target features, and obtain the target video corresponding to the object to be retrieved.

[0074] In some embodiments, video features are stored in extended information of the video to be retrieved.

[0075] In some embodiments, the extraction module 701 is further configured to extract video features from the video to be retrieved; and store the video features in the form of structured data in the extended information of the video to be retrieved.

[0076] In some embodiments, the retrieval module 702 is further configured to: traverse the video features stored in the video to be retrieved; if the video features match the target features, extract at least one video segment corresponding to the video features in the video to be retrieved; and generate a target video based on at least one video segment.

[0077] In some embodiments, the retrieval module 702 is further configured to retrieve video frames of the video to be retrieved based on video features that match the target features; if the video frame contains video features, then extract the video frame; and connect the extracted video frames sequentially according to their arrangement order in the video to be retrieved to generate at least one video segment.

[0078] In some embodiments, the retrieval module 702 is further configured to sequentially connect at least one video segment according to the order in which at least one video segment is arranged in the video to be retrieved, thereby generating a target video.

[0079] In some embodiments, the retrieval module 702 is further configured to: obtain a retrieval object, the retrieval object including at least one of retrieval text and retrieval image; extract text feature information from the retrieval text, and / or extract image feature information from the retrieval image; the target features include at least one of text feature information and image feature information.

[0080] It should be noted that the video retrieval device provided in the above embodiments is only illustrated by the division of the above functional modules when used for video retrieval. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the video retrieval device and the video retrieval method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0081] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0082] The following reference Figure 8 To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of the embodiments disclosed herein.

[0083] like Figure 8 As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0084] The storage unit stores program code, which can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0085] In some embodiments, the processing unit 810 may perform the following steps of the above method embodiment: extracting target features of the object to be retrieved; and based on the target features, retrieving video features stored in the video to be retrieved to obtain the target video corresponding to the object to be retrieved.

[0086] Storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include a read-only memory (ROM) 8203.

[0087] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0088] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0089] The electronic device 800 can also communicate with one or more external devices 840 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with the electronic device 800, and / or with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed through the input / output (I / O) interface 850. Furthermore, the electronic device 800 can also communicate via a network.

[0090] Network adapter 860 communicates with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet). As shown in the figure, network adapter 860 communicates via bus 830.

[0091] Communicates with other modules of the electronic device 800. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0092] 5. Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, etc.).

[0093] The method includes several instructions, which are contained in a portable hard drive or on a network, to cause a computing device (which may be a personal computer, server, terminal device, or network device, etc.) to perform the method according to the embodiments of the present disclosure.

[0094] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. It stores information thereon capable of...

[0095] A program product implementing the methods described above. In some possible implementations, each of the five aspects of this disclosure can also be implemented as a program product comprising program code, which, when the program product is finalized...

[0096] When run on a terminal device, the program code is used to cause the terminal device to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure.

[0097] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0098] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0099] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0100] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0101] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0102] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0103] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0104] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A video retrieval method, characterized in that, include: Extract the target features of the object to be retrieved; Based on the target features, the video features stored in the video to be retrieved are searched to obtain the target video corresponding to the object to be retrieved; The method further includes: Extract video features from the video to be retrieved, wherein the video features are extracted synchronously when the video to be retrieved is captured; The video features are stored as structured data in the extended information of the video to be retrieved.

2. The method according to claim 1, characterized in that, The step of retrieving video features stored in the video to be retrieved based on the target features to obtain the target video corresponding to the object to be retrieved includes: Iterate through the video features stored in the video to be retrieved; If the video feature matches the target feature, then at least one video segment corresponding to the video feature in the video to be retrieved is extracted; The target video is generated based on the at least one video segment.

3. The method according to claim 2, characterized in that, Extracting at least one video segment corresponding to the video features in the video to be retrieved includes: Retrieve video frames of the video to be retrieved based on the video features that match the target features; If the video frame contains the video feature, then the video frame is extracted; The extracted video frames are sequentially connected according to their arrangement order in the video to be retrieved to generate at least one video segment.

4. The method according to claim 2, characterized in that, The step of generating the target video based on the at least one video segment includes: The at least one video segment is sequentially connected according to its arrangement order in the video to be retrieved to generate the target video.

5. The method according to claim 1, characterized in that, The extraction of target features of the object to be retrieved includes: Obtain the object to be retrieved, wherein the object to be retrieved includes at least one of text to be retrieved and image to be retrieved; Extract text feature information from the text to be retrieved, and / or extract image feature information from the image to be retrieved; The target features include at least one of the text feature information and the image feature information.

6. A video retrieval device, characterized in that, include: The extraction module is used to extract the target features of the object to be retrieved; The retrieval module is used to retrieve video features stored in the video to be retrieved based on the target features, and obtain the target video corresponding to the object to be retrieved; Extract video features from the video to be retrieved; The video features are extracted synchronously when the video to be retrieved is captured; The video features are stored as structured data in the extended information of the video to be retrieved.

7. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the video retrieval method according to any one of claims 1 to 5 by executing the executable instructions.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the video retrieval method according to any one of claims 1 to 5.

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