Image acquisition method and device based on motion features, equipment and medium

Abstract

The present disclosure provides a motion feature-based image acquisition method, device, equipment and medium, relating to the technical field of image processing, the method comprising: acquiring current motion data collected by a motion sensor within a preset time length before the current time; determining a current feature value of at least one motion feature of an electronic device according to the current motion data; determining a current image recording value according to the current feature value of the at least one motion feature; and controlling an image sensor to acquire an image if the current image recording value is greater than or equal to a preset image recording value. This method can avoid recording images with no image recording value and missing key frames with image recording value.

Description

Technical Field

[0001] This disclosure relates to the field of image processing technology, and more specifically, to an image acquisition method, apparatus, device, and medium based on motion features. Background Technology

[0002] With the development of technology and economy, AI glasses are widely used in long-term recording scenarios such as security patrol, first-person perspective recording, and life logs.

[0003] Currently, AI glasses use a fixed-frequency frame-capturing mode. Specifically, the AI ​​glasses control the camera to capture images at fixed time intervals. However, this method of capturing images at fixed time intervals easily misses crucial moments that are valuable for recording, resulting in gaps in the recorded content and an inability to fully reconstruct the scene. Summary of the Invention

[0004] One object of this disclosure is to provide a new technical solution for image acquisition based on motion features.

[0005] According to a first aspect of this disclosure, a motion-feature-based image acquisition method is provided, applied to an electronic device, the electronic device including a motion sensor and an image sensor, comprising: Acquire the current motion data collected by the motion sensor within a preset time period before the current moment; Based on the current motion data, determine the current feature value of at least one motion feature of the electronic device; The current image recording value is determined based on the current feature value of the at least one motion feature; When the current image recording value is greater than or equal to the preset image recording value, the image sensor is controlled to acquire an image.

[0006] Optionally, the motion features include at least two of motion intensity, motion smoothness, and motion pattern, and determining the current image recording value based on the current feature value of the at least one motion feature includes: Normalization is performed on the current feature value of each motion feature to obtain the current normalized feature value of each motion feature; Based on the target weight coefficient combination, the weighted sum of the current normalized feature values ​​of each motion feature is determined as the current image recording value.

[0007] Optionally, before controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, the method further includes: Obtain motion avoidance feature values; The step of controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value includes: When the current image recording value is greater than or equal to the preset image recording value, and the feature value of at least one motion feature does not meet the motion avoidance feature value, the image sensor is controlled to acquire an image.

[0008] Optionally, obtaining motion avoidance feature values ​​includes: Receive image processing operations for a historical image set, the image processing operations including deletion and / or editing, the historical image set including images acquired by the image sensor at historical moments; In response to the image processing operation, motion avoidance feature values ​​are identified.

[0009] Optionally, before controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, the method further includes: Obtain the current status parameter value of the electronic device, wherein the status parameter is the battery level or remaining storage capacity; Based on the current state parameter value, a preset image recording value and / or the target weight coefficient combination that matches the current state parameter value are determined, wherein the current state parameter value is inversely proportional to the weight coefficient in the preset image recording value and the target weight coefficient combination.

[0010] Optionally, before controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, the method further includes: Get the current working mode; Based on the current working mode, determine a preset image recording value and / or the target weight coefficient combination that matches the current working mode.

[0011] Optionally, before acquiring the current motion data collected by the motion sensor within a preset time period before the current moment, the method further includes: Obtain the image acquisition mode; When the image acquisition mode is dynamic acquisition, the step of acquiring the current motion data acquired by the motion sensor within a preset time period before the current moment is triggered; When the image acquisition mode is static acquisition, the image sensor is controlled to acquire images according to a set time interval.

[0012] According to a second aspect of this disclosure, a motion-feature-based image acquisition device is provided, applied to an electronic device, the electronic device including a motion sensor and an image sensor, comprising: The acquisition module is used to acquire the current motion data collected by the motion sensor within a preset time period before the current moment; The first determining module is used to determine the current feature value of at least one motion feature of the electronic device based on the current motion data. The second determining module is used to determine the current image recording value based on the current feature value of the at least one motion feature; The control module is used to control the image sensor to acquire images when the current image recording value is greater than or equal to a preset image recording value.

[0013] According to a third aspect of this disclosure, an electronic device is provided, the electronic device comprising the means as described in the second aspect; Alternatively, the electronic device includes a memory and a processor, the memory for storing computer instructions and the processor for retrieving the computer instructions from the memory to perform the method as described in any one of the first aspects.

[0014] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of the first aspects.

[0015] This disclosure provides a motion feature-based image acquisition method applied to an electronic device, which includes a motion sensor and an image sensor. The method includes: acquiring current motion data collected by the motion sensor within a preset time period prior to the current moment; determining the current feature value of at least one motion feature of the electronic device based on the current motion data; determining the current image recording value based on the current feature value of the at least one motion feature; and controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value. This method avoids recording images without image recording value, thus avoiding the problems of occupying storage space in the electronic device and increasing the pressure on subsequent image processing. It also avoids missing key scenes with image recording value, thus preventing gaps in the recorded content and the inability to fully reconstruct the scene, achieving accurate image acquisition.

[0016] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present disclosure and, together with their description, serve to explain the principles of the present disclosure.

[0018] Figure 1 This is a schematic flowchart of an image acquisition method based on motion features provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of an image acquisition device based on motion features provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0019] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0020] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0021] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0022] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0024] This disclosure provides an image acquisition method based on motion features, applied to an electronic device. The electronic device includes a motion sensor for acquiring motion data and an image sensor for image acquisition. The electronic device is exemplarily AI glasses; however, it can also be other types of devices, and this disclosure does not limit the specific form of the electronic device.

[0025] like Figure 1 As shown, the image acquisition method based on motion features provided in this disclosure includes the following steps S1100 to S1400.

[0026] Step S1100: Obtain the current motion data collected by the motion sensor within a preset time period before the current moment.

[0027] In one embodiment of this disclosure, the motion sensor is exemplarily an inertial measurement unit (IMU). The preset duration can range from [10, 100] ms. In one example, the preset duration is exemplarily 50 ms. Of course, the preset duration can also be other values, and this disclosure does not limit the specific value of the preset duration.

[0028] Specifically, for step S1100 above, motion sensing data collected sequentially by the motion sensor within a preset time period from the current moment is read from the motion sensor and used as the current motion data, thereby realizing step S1100 above.

[0029] Step S1200: Determine the current feature value of at least one motion feature of the electronic device based on the current motion data.

[0030] Among them, at least one of the following motion characteristics is motion intensity, motion smoothness, and motion pattern.

[0031] In one embodiment of this disclosure, the current motion data may be first filtered, and then the current feature value of at least one motion feature of the electronic device may be determined based on the filtered current motion data.

[0032] Since electronic devices typically involve left-right rotational motion—for example, a user wearing AI glasses turning their head left and right—to reduce data computation, this embodiment only acquires the current angular velocity data in the image plane width direction as the current motion data. Based on this, in one embodiment, for motion intensity, the average value of the angular velocities corresponding to the current angular velocity data within a preset time period before the current moment can be determined as the motion characteristic value of motion intensity. The larger the aforementioned average value, the faster the electronic device rotates, i.e., the greater the motion intensity. Further, for motion smoothness, the standard deviation of the angular velocities corresponding to the current angular velocity data within a preset time period before the current moment can be determined as the motion characteristic value of motion smoothness. The larger the aforementioned standard deviation, the less smooth the rotation of the electronic device. And, for motion mode, if the aforementioned average value is less than or equal to a first threshold and the standard deviation is less than or equal to a second threshold, the motion characteristic value of motion mode is determined to be stationary. If the aforementioned standard deviation is less than or equal to the second threshold, but the aforementioned average value is greater than the first threshold, the motion characteristic value of motion mode is determined to be uniform rotation. If the aforementioned standard deviation is greater than or equal to the third threshold, and the aforementioned average value is greater than or equal to the fourth threshold, then the motion characteristic value of the motion pattern is determined to be accelerated rotation. If the aforementioned standard deviation is greater than or equal to the third threshold, but the aforementioned average value is less than the fourth threshold, then the motion characteristic value of the motion pattern is determined to be jitter.

[0033] The fourth threshold is greater than the first threshold, the third threshold is greater than the second threshold, and the first, second, third, and fourth thresholds are determined experimentally. The first threshold is the maximum value of the aforementioned average when the electronic device is stationary, the second threshold is the maximum value of the aforementioned standard deviation when the electronic device is stationary, the third threshold is the minimum value of the aforementioned standard deviation when the electronic device is accelerating or vibrating, and the fourth threshold is the minimum value of the aforementioned average when the electronic device is accelerating and rotating.

[0034] In another embodiment of this disclosure, deep learning can be used to implement step S1200 above. Specifically, a training sample set is obtained, wherein the training sample set includes multiple sets of training samples, each set of training samples includes sample motion data and corresponding sample motion feature values, and each sample motion feature value includes at least one sample feature value of a motion feature; a preset neural network model is trained based on the training sample set to obtain a motion feature prediction model. Based on this, the current motion data is input into the motion feature prediction model, and the motion feature prediction model outputs the current feature value of at least one motion feature. The sample motion feature values ​​corresponding to the sample motion data are collected by: performing motion feature value recognition on the sample motion data to obtain the feature value of at least one motion feature, denoted as the sample feature value of at least one motion feature.

[0035] Step S1300: Determine the current image recording value based on the current feature value of at least one motion feature.

[0036] In this embodiment, the feature values ​​of motion features can reflect the user's visual attention switching behavior. However, visual attention switching behavior can indicate whether the current scene is worth recording. Therefore, the value of image recording can be quantified through the feature values ​​of motion features.

[0037] The current image recording value is used to quantify the degree to which an image in the current scene is worth recording; the more worthwhile an image is to be recorded, the greater the current image recording value.

[0038] In one embodiment of this disclosure, when there is only one motion feature, the current feature value of that motion feature is directly normalized, and the normalized current feature value is recorded as the current image recording feature value. For example, if the motion feature value corresponding to motion mode F_c is uniform rotation and the corresponding normalized motion feature value is 0.7, the current image recording feature value is 0.7. If the motion feature value corresponding to motion mode F_c is stationary and the corresponding normalized motion feature value is 0, the current image recording feature value is 0. If the motion feature value corresponding to motion mode F_c is accelerated rotation and the corresponding normalized motion feature value is 0.9, the current image recording feature value is 0.9. If the motion feature value corresponding to motion mode F_c is jitter and the corresponding normalized motion feature value is 0.2, the current image recording feature value is 0.2.

[0039] In one embodiment of this disclosure, when the motion characteristics include at least two of motion intensity, motion smoothness, and motion pattern, the above step S1300 can be specifically implemented by the following steps S1310 and S1311.

[0040] Step S1310: Perform normalization processing on the current feature value of each motion feature to obtain the current normalized feature value of each motion feature.

[0041] Through the above step S1310, motion feature values ​​of different dimensions are uniformly mapped to the [0,1] interval, which facilitates subsequent weighted summation.

[0042] Step S1311: According to the target weight coefficient combination, the weighted sum of the current normalized feature values ​​of each motion feature is determined as the current image recording value.

[0043] The target weight coefficient combination includes a weight coefficient for each motion feature. In one embodiment of this disclosure, the target weight coefficient combination is a default combination of weight coefficients. For example, in the target weight coefficient combination, the weight coefficient w3=0.5 for motion intensity F_a, the weight coefficient w2=-0.3 for motion stability F_b, and the weight coefficient w1=1 for motion mode F_c.

[0044] Taking motion features including motion intensity, motion stability, and motion pattern as an example, if the motion feature value corresponding to motion intensity F_a is 20° / s and the corresponding normalized motion feature value is 0.4, the motion feature value corresponding to motion stability F_b is 2° / s and the corresponding normalized motion feature value is 0.1, and the motion feature value corresponding to motion pattern F_c is uniform rotation and the corresponding normalized motion feature value is 0.7, and the target weight coefficient combination is that the weight coefficient of motion intensity F_a is w3=0.5, the weight coefficient of motion stability F_b is w2=-0.3, and the weight coefficient of motion pattern F_c is w1=1, then the current image recording value S=1*0.7+(-0.3)*0.1+0.5*0.4=0.7-0.03+0.2=0.87.

[0045] Taking the motion mode as a shake as an example, the normalized feature value corresponding to the motion stability F_b is relatively large, but the weight coefficient of motion stability F_b is w2=-0.3, which has a large negative contribution, thus lowering the current image recording value. This can effectively suppress false image acquisition caused by shake. Continuing with the motion mode as an accelerated rotation example, the current normalized feature value (0.9) corresponding to the motion mode is very large. Although the weight coefficient of motion stability F_b is negative, the current image recording value can still easily exceed the preset image recording value. Based on this, it is possible to ensure the capture of fast movements.

[0046] After step S1300 above, the following steps S1400 and S1500 are performed to determine the current image recording value and the preset image recording value.

[0047] Step S1400: If the current image recording value is greater than or equal to the preset image recording value, control the image sensor to acquire an image.

[0048] The preset image recording value is the minimum image recording value when the image is worth recording, and it can be determined based on experience, experiments, or scenarios.

[0049] If the current image recording value is greater than or equal to the preset image recording value, then the image is deemed worth recording. At this point, the image sensor is controlled to acquire the image. After step S1400, if the current time changes, step S1100 is repeated to re-determine the difference between the current image recording value and the preset image recording value.

[0050] Corresponding to step S1400 above, the motion feature-based image acquisition method provided in this disclosure further includes the following step S1500.

[0051] In step S1500, if the current image recording value is less than the preset image recording value, the image sensor is not controlled to acquire images.

[0052] Corresponding to step S1400 above, if the current image recording value is less than the preset image recording value, it means that the image is not worth recording. At this time, in order to avoid the image sensor acquiring an image with no recording value, the image sensor is not controlled to acquire an image. Instead, step S1100 is repeated under the condition that the current time changes, so as to re-determine the current image recording value and the preset image recording value.

[0053] It should be noted that the electronic device executes the above step S1100 in real time as the current moment is updated.

[0054] As described in steps S1100 to S1400 above, this disclosure provides a dynamic image acquisition method based on motion features. This method quantifies the recording degree reflecting the image's value based on current motion data to obtain the current image recording value. Image acquisition is only initiated when the current image recording value is sufficient. This avoids recording images without recording value, thus avoiding the problems of occupying electronic device storage space and increasing subsequent image processing pressure. It also avoids missing key scenes with recording value, resulting in fragmented recording content and an inability to fully reconstruct the scene, thus achieving precise image acquisition.

[0055] This disclosure provides a motion feature-based image acquisition method applied to an electronic device, which includes a motion sensor and an image sensor. The method includes: acquiring current motion data collected by the motion sensor within a preset time period prior to the current moment; determining the current feature value of at least one motion feature of the electronic device based on the current motion data; determining the current image recording value based on the current feature value of the at least one motion feature; and controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value. This method avoids recording images without image recording value, thus avoiding the problems of occupying storage space in the electronic device and increasing the pressure on subsequent image processing. It also avoids missing key scenes with image recording value, thus preventing gaps in the recorded content and the inability to fully reconstruct the scene, achieving accurate image acquisition.

[0056] In one embodiment of this disclosure, the motion feature-based image acquisition method provided by this disclosure further includes the following step S1600 before the above steps S1400 and S1500.

[0057] Step S1600: Obtain motion avoidance feature values.

[0058] Motion avoidance features are used to describe the types of motion features that tend to prevent image acquisition, thus eliminating invalid triggering scenarios. An example of a motion avoidance feature is a jittery motion pattern; however, other motion avoidance features are also possible, and this disclosure does not limit the specific form of the motion avoidance feature.

[0059] In one embodiment of this disclosure, motion avoidance feature values ​​may be pre-input into the electronic device by the user.

[0060] In another embodiment of this disclosure, the motion avoidance feature value can be obtained by the electronic device learning from image processing operations performed by the user on images historically acquired by the image sensor. Specifically, step S1600 is implemented through steps S1610 and S1611 described below.

[0061] Step S1610: Receive image processing operations for the historical image set, including deletion and / or editing.

[0062] Step S1611: In response to the image processing operation, identify and acquire motion avoidance feature values.

[0063] In this embodiment of the disclosure, the historical image set includes images acquired by the image sensor at historical moments. When a user views images in the historical image set, they typically perform image processing operations. For example, they may delete images that do not meet their needs or edit images to better suit their requirements. Based on this, the electronic device can receive the user's image processing operations on the historical image set.

[0064] It is understood that image processing operations performed by a user on images in a historical image set can reflect motion avoidance feature values. In one embodiment of this disclosure, an AI model can be used to identify the image corresponding to the image processing operation in order to extract motion avoidance feature values.

[0065] As can be seen from the above steps S1610 and S1611, the motion feature-based image acquisition method provided in this embodiment can automatically learn and update the user's motion avoidance feature value by monitoring the user's deletion and editing operations on images acquired at historical moments. This realizes the automatic acquisition of personalized motion avoidance feature values ​​without the need for active user configuration, thereby improving the intelligence of image acquisition and user experience.

[0066] Based on the above step S1600, the above step S1400 is specifically implemented through the following step S1410.

[0067] Step S1410: When the current image recording value is greater than or equal to the preset image recording value, and the feature value of at least one motion feature does not meet the motion avoidance feature value, control the image sensor to acquire an image.

[0068] In this embodiment, the system determines whether the current image recording value is greater than or equal to a preset image recording value, and whether the feature value of at least one motion feature meets the motion avoidance feature value. If the current image recording value is greater than or equal to the preset image recording value, and the feature value of at least one motion feature does not meet the motion avoidance feature value, then the image sensor is controlled to acquire an image. This further eliminates motion scenarios that the user does not want to trigger, improves the triggering accuracy of the image sensor, and reduces the acquisition of invalid images.

[0069] Corresponding to step S1410 above, step S1500 above is specifically implemented through step S1510 below.

[0070] In step S1510, if the current image recording value is less than the preset image recording value and / or the feature value of at least one motion feature meets the motion avoidance feature value, the image sensor is not controlled to acquire images.

[0071] In one embodiment of this disclosure, the motion feature-based image acquisition method provided by this disclosure further includes the following steps S1700 and S1800 before the above steps S1400 and S1500.

[0072] Step S1700: Obtain the current state parameter value of the electronic device.

[0073] Among them, the status parameter is the battery level or the remaining storage capacity.

[0074] Step S1800: Based on the current state parameter value, determine the preset image recording value and / or target weight coefficient combination that matches the current state parameter value.

[0075] Among them, the current state parameter value is inversely proportional to the preset image recording value, and the current state parameter value is inversely proportional to the weight coefficient in the target weight coefficient combination.

[0076] In one embodiment of this disclosure, the electronic device pre-stores a preset mapping relationship that reflects the correspondence between different state parameter value ranges and corresponding preset image recording values ​​and / or target weight coefficient combinations. Based on this, after obtaining the current state parameter value, the preset image recording value and / or target weight coefficient combination corresponding to the state parameter value range to which the current state parameter value belongs in the preset mapping relationship is recorded as the preset image recording value and / or target weight coefficient combination that matches the current state parameter value.

[0077] Taking battery level as a status parameter as an example, since the current status parameter value is inversely proportional to the preset image recording value, the lower the current battery level, the higher the preset image recording value. In this case, the time interval between image sensor acquisitions can be increased, thereby reducing the number of image acquisitions and helping to reduce the power consumption of the electronic device and improve its battery life. Similarly, when the status parameter is remaining storage capacity, the lower the current remaining storage capacity, the higher the preset image recording value. In this case, the time interval between image sensor acquisitions can be increased, thereby reducing the number of image acquisitions and helping to save storage capacity. The same principle applies to the combination of target weighting coefficients.

[0078] As can be seen from steps S1700 and S1800 above, the image acquisition method provided in this disclosure can also adaptively adjust the image acquisition time interval according to the current state of the electronic device. This also takes into account the battery life and storage capacity of the electronic device.

[0079] In one embodiment of this disclosure, the motion feature-based image acquisition method provided by this disclosure further includes the following steps S1900 and S11000 before the above steps S1400 and S1500.

[0080] Step S1900: Obtain the current working mode.

[0081] The current working mode can include, for example, sports recording mode, document scanning mode, and daily wear mode. Of course, other modes may also be included, and this disclosure does not limit the specific type of the current working mode. Users can input the current working mode into the electronic device via voice commands, touch operation, or remote settings via a companion app. Based on this, step S1900 described above is achieved.

[0082] Step S11000: Based on the current working mode, determine the preset image recording value and / or target weight coefficient combination that matches the current working mode.

[0083] In this embodiment of the disclosure, experiments can be conducted based on the different requirements of image recording sensitivity and motion feature weights for different working modes to determine the preset image recording value and / or target weight coefficient combination corresponding to different working modes.

[0084] Through the above steps S11000, the most suitable combination of preset image recording value and / or target weight coefficients for the current working mode can be dynamically determined according to the requirements of the current working mode for image recording sensitivity and motion feature weight, so that the image acquisition strategy can adapt to different working modes.

[0085] In one example, for the preset image recording value, the motion recording mode corresponds to a preset image recording value of 0.6. At this value, the image sensor has high trigger sensitivity, suitable for capturing fast-moving scenes. The document scanning mode corresponds to a preset image recording value of 0.8. At this value, the image sensor has low trigger sensitivity, avoiding a large number of redundant images generated by text scanning. The constant wear mode corresponds to a preset image recording value of 0.7. At this value, the image sensor has moderate trigger sensitivity, balancing recording integrity and power consumption.

[0086] For the target weight coefficient combination, the motion recording mode corresponds to the following combination: w1=1, w2=-0.2, w3=0.5, emphasizing the positive contribution of motion intensity, suitable for capturing fast-moving scenes. The document scanning mode corresponds to the following combination: w1=0.7, w2=-0.5, w3=0.8, emphasizing the contribution of motion mode and strongly suppressing jitter, suitable for text recording scenes. The daily wear mode corresponds to the following combination: w1=0.5, w2=-0.3, w3=0.7, balancing the weights of each feature, suitable for general daily recording. Of course, the target weight coefficient combination can also be other values, and this disclosure does not limit the specific values ​​of the target weight coefficient combination.

[0087] As can be seen from the above steps S1900 and S11000, this embodiment of the present disclosure dynamically determines the preset image recording value and / or target weight coefficient combination according to the current working mode, so that the trigger sensitivity and feature weight of image acquisition can be automatically adjusted according to the current working mode. This improves the intelligence of the motion feature-based image acquisition method provided by this embodiment of the present disclosure.

[0088] In one embodiment of this disclosure, the motion feature-based image acquisition method provided by this disclosure further includes the following steps S1110 to S1112 before the above step S1100.

[0089] Step S1110: Obtain the image acquisition mode.

[0090] In this embodiment, the electronic device supports two image acquisition modes: dynamic acquisition mode and static acquisition mode. Users can input the current image acquisition mode to the electronic device via voice commands, touch operation, or remote settings via a companion app. Based on this, step S1110 described above is achieved.

[0091] Step S1111: When the image acquisition mode is dynamic acquisition, trigger the step of acquiring the current motion data acquired by the motion sensor within a preset time period before the current moment.

[0092] Step S1112: When the image acquisition mode is static acquisition, control the image sensor to acquire images according to the set time interval.

[0093] In this embodiment of the disclosure, if the current image acquisition mode is dynamic acquisition mode, then step S1100 is triggered to execute the dynamic image acquisition method.

[0094] If the current image acquisition mode is static acquisition mode, the image sensor is controlled to acquire images according to the set time interval, and the electronic device no longer acquires current motion data. The set time interval is 5 seconds as an example, but other values ​​are also possible; this disclosure does not limit the specific value of the set time interval. Static acquisition mode is suitable for scenarios where recording at a fixed frequency is desired, such as time-lapse photography or periodic monitoring.

[0095] Through the above steps S1110 to S1112, the motion feature-based image acquisition method provided by this embodiment supports both dynamic and static acquisition modes, meeting the needs of different application scenarios and improving the intelligence of electronic devices.

[0096] like Figure 2As shown, this disclosure also provides an image acquisition device 200 based on motion features, applied to an electronic device, the electronic device including a motion sensor and an image sensor, the image acquisition device 200 based on motion features including: The acquisition module 210 is used to acquire the current motion data collected by the motion sensor within a preset time period before the current moment; The first determining module 220 is used to determine the current feature value of at least one motion feature of the electronic device based on the current motion data. The second determining module 230 is used to determine the current image recording value based on the current feature value of the at least one motion feature; Control module 240 is used to control the image sensor to acquire images when the current image recording value is greater than or equal to a preset image recording value.

[0097] In one embodiment of this disclosure, the motion characteristics include at least two of motion intensity, motion smoothness, and motion pattern; the second determining module 230 is specifically used for: Normalization is performed on the current feature value of each motion feature to obtain the current normalized feature value of each motion feature; Based on the target weight coefficient combination, the weighted sum of the current normalized feature values ​​of each motion feature is determined as the current image recording value.

[0098] In one embodiment of this disclosure, the acquisition module 210 is further configured to: acquire motion avoidance feature values; And, control module 240, specifically used to: control the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, and the feature value of at least one motion feature does not meet the motion avoidance feature value.

[0099] In one embodiment of this disclosure, the acquisition module 210 is specifically used for: Receive image processing operations for a historical image set, the image processing operations including deletion and / or editing, the historical image set including images acquired by the image sensor at historical moments; In response to the image processing operation, motion avoidance feature values ​​are identified.

[0100] In one embodiment of this disclosure, the acquisition module 210 is further configured to: Obtain the current status parameter value of the electronic device, wherein the status parameter is the battery level or remaining storage capacity; In this embodiment of the disclosure, the motion feature-based image acquisition device 200 provided further includes: The third determining module is used to determine, based on the current state parameter value, a preset image recording value and / or the target weight coefficient combination that matches the current state parameter value, wherein the current state parameter value is inversely proportional to the weight coefficient in the preset image recording value and the target weight coefficient combination.

[0101] In one embodiment of this disclosure, the acquisition module 210 is further configured to: acquire the current working mode; In this embodiment of the disclosure, the motion feature-based image acquisition device 200 provided further includes: The fourth determining module is used to determine, based on the current working mode, a preset image recording value and / or the target weight coefficient combination that matches the current working mode.

[0102] In one embodiment of this disclosure, the acquisition module 210 is further configured to: Obtain the image acquisition mode; The control module 240 is also used to trigger the step of acquiring the current motion data acquired by the motion sensor within a preset time period before the current moment when the image acquisition mode is dynamic acquisition. When the image acquisition mode is static acquisition, the image sensor is controlled to acquire images according to a set time interval.

[0103] This disclosure also provides an electronic device, which includes a motion-feature-based image acquisition device 200 as provided in any of the above-described device embodiments.

[0104] like Figure 3 As shown, this disclosure also provides another electronic device 300, which includes a motion sensor 310, an image sensor 320, a memory 330, and a processor 340. The motion sensor 310 is used to collect motion data of the electronic device 300, the image sensor 320 is used to collect images, the memory 330 is used to store computer instructions, and the processor 340 is used to call the computer instructions from the memory 330 to execute the method as described in any of the above method embodiments.

[0105] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of the above-described method embodiments.

[0106] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0107] This disclosure may be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement any of the methods in the foregoing embodiments of this disclosure.

[0108] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media may include, for example, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), compact disc-read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any combination thereof. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0109] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include one or more of copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media in the respective computing / processing device.

[0110] The computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object programs written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network (e.g., a local area network or a wide area network), or it may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays, or programmable logic arrays, can execute computer-readable program instructions to implement various aspects of the embodiments of this disclosure by utilizing state information from the computer-readable program instructions.

[0111] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0112] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0113] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It should be noted that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are all equivalent.

[0115] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of this disclosure is defined by the appended claims.

Claims

1. An image acquisition method based on motion features, characterized in that, Applied to electronic devices, the electronic devices including motion sensors and image sensors, including: Acquire the current motion data collected by the motion sensor within a preset time period before the current moment; Based on the current motion data, determine the current feature value of at least one motion feature of the electronic device; The current image recording value is determined based on the current feature value of the at least one motion feature; If the current image recording value is greater than or equal to the preset image recording value, control the image sensor to acquire an image; The step of determining the current image recording value based on the current feature value of the at least one motion feature includes: Normalization is performed on the current feature value of each motion feature to obtain the current normalized feature value of each motion feature; According to the target weight coefficient combination, the weighted sum of the current normalized feature values ​​of each motion feature is determined as the current image recording value; Before controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, the method further includes: Get the current working mode; Based on the current working mode, determine a preset image recording value and / or the target weight coefficient combination that matches the current working mode.

2. The method according to claim 1, characterized in that, The motion characteristics include at least two of the following: motion intensity, motion smoothness, and motion pattern.

3. The method according to claim 1, characterized in that, Before controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, the method further includes: Obtain motion avoidance feature values; The step of controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value includes: When the current image recording value is greater than or equal to the preset image recording value, and the feature value of at least one motion feature does not meet the motion avoidance feature value, the image sensor is controlled to acquire an image.

4. The method according to claim 3, characterized in that, The acquisition of motion avoidance feature values ​​includes: Receive image processing operations for a historical image set, the image processing operations including deletion and / or editing, the historical image set including images acquired by the image sensor at historical moments; In response to the image processing operation, motion avoidance feature values ​​are identified.

5. The method according to claim 1, characterized in that, Before controlling the image sensor to acquire an image when the current image recording value is greater than or equal to a preset image recording value, the method further includes: Obtain the current status parameter value of the electronic device, wherein the status parameter is the battery level or remaining storage capacity; Based on the current state parameter value, a preset image recording value and / or the target weight coefficient combination that matches the current state parameter value are determined, wherein the current state parameter value is inversely proportional to the weight coefficient in the preset image recording value and the target weight coefficient combination.

6. The method according to claim 1, characterized in that, Before acquiring the current motion data collected by the motion sensor within a preset time period prior to the current moment, the method further includes: Obtain the image acquisition mode; When the image acquisition mode is dynamic acquisition, the step of acquiring the current motion data acquired by the motion sensor within a preset time period before the current moment is triggered; When the image acquisition mode is static acquisition, the image sensor is controlled to acquire images according to a set time interval.

7. An image acquisition device based on motion features, characterized in that, Applied to electronic devices, the electronic devices including motion sensors and image sensors, including: The acquisition module is used to acquire the current motion data collected by the motion sensor within a preset time period before the current moment; The first determining module is used to determine the current feature value of at least one motion feature of the electronic device based on the current motion data. The second determining module is used to determine the current image recording value based on the current feature value of the at least one motion feature; The control module is used to control the image sensor to acquire images when the current image recording value is greater than or equal to a preset image recording value; Specifically, the second determining module is used to perform normalization processing on the current feature value of each motion feature to obtain the current normalized feature value of each motion feature. According to the target weight coefficient combination, the weighted sum of the current normalized feature values ​​of each motion feature is determined as the current image recording value; Furthermore, the acquisition module is also used to acquire the current working mode; The motion feature-based image acquisition device further includes: The fourth determining module is used to determine, based on the current working mode, a preset image recording value and / or the target weight coefficient combination that matches the current working mode.

8. An electronic device, characterized in that, The electronic device includes the apparatus as described in claim 7; Alternatively, the electronic device includes a memory and a processor, the memory for storing computer instructions, and the processor for retrieving the computer instructions from the memory to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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