Target video capturing method and device based on image screening, equipment and medium

Abstract

The invention discloses a target video capturing method, device and equipment based on image screening and a medium, and relates to the technical field of monitoring analysis, and the target video capturing method comprises a user side input module, a sampling interval setting module and a picture acquisition module; according to the target video capture method and device based on image screening, the equipment and the medium, through the image segmentation module, the image labeling module and the data judgment module, and through the importance degree calculation module and the continuous change judgment module, the influence degree of the change of the current image on target video capture can be judged in real time; furthermore, whether the current video image is continuously changed or not can be accurately judged through a continuous change judgment module during video capturing, and the situation of omission during video capturing can be avoided by capturing the continuously changed video; and meanwhile, video capturing can be accurately carried out through capturing in the changing process of different movable objects, and the situation that after video capturing is carried out, manual watching and screening are needed is avoided.

Description

Technical Field

[0001] The present invention relates to the field of monitoring and analysis technology, and in particular to a target video capture method, device, equipment and medium based on image screening. Background Art

[0002] The traditional monitoring system includes front-end cameras, transmission cables, and video monitoring platforms. Cameras can be divided into network digital cameras and analog cameras, which can be used to collect front-end video image signals. It is a comprehensive system with strong prevention capabilities. Video monitoring is widely used in many occasions because of its intuitiveness, accuracy, timeliness, and rich information content.

[0003] In recent years, with the rapid development of computers, networks, image processing and transmission technologies, video surveillance technology has also made great progress. Current video surveillance equipment usually records videos continuously. Continuous recording makes the video files larger. Large video files store massive amounts of video data. The resulting technical problem is that when capturing target videos based on images, only video data within a period of time before and after the current image can be captured. This will result in the need for manual viewing and screening of the captured videos after video capture. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, equipment and medium for capturing target video based on image screening to solve the above-mentioned deficiencies in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a target video capture method based on image screening, comprising the following steps: S1, the user inputs a query image through an input terminal, and the query image is used as a standard image; S2, set the sampling interval; S3, acquiring an image within the sampling interval, and performing screen segmentation on the image within the sampling interval and the standard image based on screen segmentation, and dividing the image within the sampling interval and the standard image into a movable object and a fixed object; S4, judging whether the movable object in the image frame within the sampling interval has changed relative to the movable object in the standard image based on the image comparison model, marking the changed image frame within the sampling interval as a changed image and marking the changed position within the image frame within the sampling interval based on the image annotation model, and marking the unchanged image frame within the sampling interval as an unchanged image; S5, judging the reason why the movable object in the changed image in step S4 changes based on the judgment model, wherein the reason for the change is not limited to human factors, environmental factors, and light factors; S6, calculating the importance of the current change based on the importance calculation model, and sorting according to the importance, presetting a detection threshold, retaining the annotation for the change image whose importance of the current change is higher than the preset detection threshold, and deleting the annotation for the change image whose importance of the current change is lower than the preset detection threshold; S7, determining whether the change of the marked change image retained in step S6 is a continuous change; S8, for the changing image judged as continuously changing in step S7, according to the change of the corresponding annotation in step S4, intercept the video with complete annotated changes, for the changing images with multiple annotated changes, intercept and classify and store according to different annotations, wherein the specific standard for video interception is a complete change process of the annotation of the changing image; S9, performing video capture authentication on each video segment captured in step S8 based on the video comparison authentication model.

[0006] Furthermore, in step S3, the image within the sampling interval and the standard image are segmented based on the image segmentation, which specifically includes the following steps: A1, segment the image, mark one part of the image as fixed objects as N, and the other part as movable objects as M. Array F[n] is used to record the labels; A2, scan the fixed object and movable object area pixel by pixel, obtain the angle value x1 of each pixel in the color vector graph, and set the value of F[x1] to 1; A3, scan the M area containing the movable object point by point, obtain the angle value x2 corresponding to the non-pixel point, calculate F[x2], if the value of the point is not 1, retain it, otherwise remove it; The movable object photo is set as P, and P(x,y) is defined as the brightness value of the point with internal coordinates (x,y) on the movable object, which is converted into polar coordinates using the formula:

[0007] Where P'(r,θ) represents the brightness change value of the point with internal coordinates (r,θ); In order to reduce the unknown influence of local features such as cracks of different degrees on movable objects on segmentation, we first perform Gaussian filtering on the image, then extract the brightness channel and hue channel from the three color attributes for polar coordinate transformation. After completion, we use the edge detection operator to perform an edge detection and use a vertical edge detector to detect the image; The detection result is converted back to the rectangular coordinate system using the following formula, and then a morphological operation is performed to obtain a closed edge:

[0008] By identifying different closed edges, the specific shape of movable objects can be detected, which facilitates image segmentation.

[0009] Furthermore, the generation of the importance model in step S6 specifically includes the following steps: B1, randomized initialization variation data set; B2, calculate the data fitness, based on the weight value and initial threshold in the back propagation neural network determined by the individual, the specific calculation formula is as follows: , Where a is the number of nodes of the variable data, y b is the prediction data of the bth node, o b is the actual data of the bth node, and c is the coefficient; B3, selection operation, using the roulette method to perform the selection operation, and then select individuals with good fitness from the variation data to form a new variation data group. The specific formula for the probability of individual selection is as follows: ; Where c is the coefficient, N is a random number, and P b represents the data fitness of the b-th node, P j Represents the data fitness of the jth node; B4, cross operation, randomly select two change data for cross operation, so that the mth change data a at the jth position m and the qth variation data a q For example, the specific formula is as follows: , Where z is a random number between [0, 1]; B5, mutation operation, randomly selects a variation data from the variation data group, and performs mutation operation according to a preset probability to obtain new variation data; B6, using the obtained optimal value to update the weight threshold, training to obtain the current change importance model.

[0010] Furthermore, the step S7 of determining whether the important change is a continuous change specifically includes the following steps: C1, first, detect each sub-annotation data spectrum in the image annotation result based on the local model; C2, if the posterior score of a sub-annotated spectrogram exceeds the threshold, it is considered a local change; C3, the system records the start time and end time of continuous local changes. The start time is the left edge of the window corresponding to the first local change, and the end time is the right edge of the window corresponding to the last local change. C4, when continuous local changes occur, it indicates that there are continuous changes in the image annotations within the time period from the start time to the end time, that is, the important changes currently detected are continuous changes; C5, if no continuous local changes occur, the currently detected important changes are considered as discontinuous changes; C6, re-inputting the non-continuous change image annotations in step C5 into step C1 for subsequent operations until there is no continuous change in the important change annotations.

[0011] Furthermore, the video capture authentication in step S9 specifically includes the following steps: D1, in response to each video segment captured in step S8, extracting image data that meets the specified image condition from each video segment; any image data that meets the specified image condition carries at least identification information and timestamp information of the beacon device that sends the image data; D2, if extracted, obtaining the specified data in the extracted image data; wherein the specified data at least includes the identification information and timestamp information of the beacon device that sent the extracted image data; D3, obtaining target identification information of each video segment intercepted in step S8, and target timestamp information of the video; D4, performing a matching analysis on the target timestamp information and the timestamp information in the specified data to obtain a first analysis result, and performing a matching analysis on the target identification information and the identification information in the specified data to obtain a second analysis result; D5, based on the first analysis result and the second analysis result, determining the authentication result of the video; wherein the authentication result is used to indicate whether each video segment captured in the step S8 is fake.

[0012] A target video capture device based on image screening comprises a user-side input module, a sampling interval setting module, a picture acquisition module, a screen segmentation module, a screen comparison module, a screen annotation module, a data judgment module, an importance degree calculation module, a data judgment module, a video capture module, a video capture authentication module, a user-side input module, a sampling interval setting module, a picture acquisition module, a screen segmentation module, a screen comparison module, a screen annotation module, a data judgment module, an importance degree calculation module, a data judgment module, a video capture module, and a video capture authentication module; The user-side input module is used for the user to input the image to be queried; The sampling interval setting module is used to set the sampling interval, and the sampling interval is the playing time period of the video; The image acquisition module is used to acquire images within a sampling interval; The screen segmentation module is used to perform screen segmentation on the image within the sampling interval and the standard image based on screen segmentation, and to segment the image within the sampling interval and the standard image into movable objects and fixed objects; The image comparison module is used to determine whether the movable object in the image frame within the sampling interval has changed relative to the movable object in the standard image; The picture annotation module is used to annotate the changed image pictures in the sampling interval as changed images and annotate the changed positions in the image pictures in the sampling interval based on the picture annotation model, and annotate the unchanged image pictures in the sampling interval as unchanged images; The data judgment module is used to judge the reason why the movable object in the image marked as a changed image changes based on the judgment model; The importance calculation module is used to calculate the importance of the current change based on the importance calculation model, and sort according to the importance, preset a detection threshold, retain the annotation for the change image whose importance of the current change is higher than the preset detection threshold, and delete the annotation for the change image whose importance of the current change is lower than the preset detection threshold; The continuous change judgment module is used to judge whether the change of the change image with the mark retained in the importance calculation module is a continuous change; The video capture module is used to capture the video with complete annotated changes according to the changes of the corresponding annotations in the picture annotation module for the changing images judged as continuously changing by the data judgment module; The video capture authentication module performs video capture authentication on each video segment captured in the video capture module based on a video comparison authentication model.

[0013] An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions; The processor implements a target video capture method when executing the computer program instructions.

[0014] A computer storage medium, characterized in that computer program instructions are stored on the computer storage medium, and the computer program instructions implement a target video capture method when executed by a processor.

[0015] Compared with the prior art, the target video capture method, device, equipment and medium based on image screening provided by the present invention can accurately identify movable objects and fixed objects in the image through the picture segmentation module, the picture annotation module and the data judgment module, and can accurately capture important parts of the video image by annotating the moving movable objects and judging the reasons for the changes of the movable objects. At the same time, the importance calculation module and the continuous change judgment module can be used to judge in real time the influence of the change of the current picture image on the target video capture, and the continuous change judgment module can be used to accurately judge whether the current video image is continuously changing during video capture. By capturing the continuously changing video, omissions can be avoided when capturing the video again. At the same time, by capturing the change process of different movable objects, video capture can be performed accurately, avoiding the need for manual viewing and screening after video capture. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 A structural flowchart of the method flow provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0019] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0020] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0021] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0022] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0023] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0024] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0025] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of idealized schematic diagrams of the present disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings have schematic properties, and the shapes of the regions shown in the figures illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0026] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0027] See also Figure 1 , a target video capture method, device, equipment and medium based on image screening, comprising the following steps: S1, the user inputs the query image through the input terminal, and the query image is used as the standard image; S2, set the sampling interval; S3, acquiring an image within the sampling interval, and performing screen segmentation on the image within the sampling interval and the standard image based on screen segmentation, and dividing the image within the sampling interval and the standard image into a movable object and a fixed object; S4, judging whether the movable object in the image frame within the sampling interval has changed relative to the movable object in the standard image based on the image comparison model, marking the changed image frame within the sampling interval as a changed image and marking the changed position within the image frame within the sampling interval based on the image annotation model, and marking the unchanged image frame within the sampling interval as an unchanged image; S5, judging the reason why the movable object in the changed image in step S4 changes based on the judgment model, the reason for the change including but not limited to human factors, environmental factors, and light factors; S6, calculating the importance of the current change based on the importance calculation model, and sorting according to the importance, presetting a detection threshold, retaining the annotation for the change image whose importance of the current change is higher than the preset detection threshold, and deleting the annotation for the change image whose importance of the current change is lower than the preset detection threshold; S7, determining whether the change of the marked change image retained in step S6 is a continuous change; S8, for the changing image judged as continuously changing in step S7, according to the change of the corresponding annotation in step S4, intercept the video with complete annotated changes, for the changing images with multiple annotated changes, intercept and classify and store according to different annotations, wherein the specific standard for video interception is a complete change process of the annotation of the changing image; S9, performing video capture authentication on each video segment captured in step S8 based on the video comparison authentication model.

[0028] In step S3, the image within the sampling interval and the standard image are segmented based on the image segmentation, which specifically includes the following steps: A1, segment the image, mark one part of the image as fixed objects as N, and the other part as movable objects as M. Array F[n] is used to record the marks; A2, scan the fixed object and movable object area pixel by pixel, obtain the angle value x1 of each pixel in the color vector graph, and set the value of F[x1] to 1; A3, scan the M area containing the movable object point by point, obtain the angle value x2 corresponding to the non-pixel point, calculate F[x2], if the value of the point is not 1, retain it, otherwise remove it; The movable object photo is set as P, and P(x,y) is defined as the brightness value of the point with internal coordinates (x,y) on the movable object, which is converted into polar coordinates using the formula:

[0029] Where P'(r,θ) represents the brightness change value of the point with internal coordinates (r,θ); In order to reduce the unknown influence of local features such as cracks of different degrees on movable objects on segmentation, we first perform Gaussian filtering on the image, then extract the brightness channel and hue channel from the three color attributes for polar coordinate transformation. After completion, we use the edge detection operator to perform an edge detection and use a vertical edge detector to detect the image; The detection result is converted back to the rectangular coordinate system using the following formula, and then a morphological operation is performed to obtain a closed edge:

[0030] By identifying different closed edges, the specific shape of movable objects can be detected, which facilitates image segmentation.

[0031] The generation of the importance model in step S6 specifically includes the following steps: B1, random initialization variation data set; B2, calculate the data fitness, based on the weight value and initial threshold in the back propagation neural network determined by the individual, the specific calculation formula is as follows: , Where a is the number of nodes of the variable data, y b is the prediction data of the bth node, o b is the actual data of the bth node, and c is the coefficient; B3, selection operation, using the roulette method to perform the selection operation, and then select individuals with good fitness from the variation data to form a new variation data group. The specific formula for the probability of individual selection is as follows: ; Where c is the coefficient, N is a random number, and P b represents the data fitness of the b-th node, P j Represents the data fitness of the jth node; B4, cross operation, randomly select two change data for cross operation, so that the mth change data a at the jth position m and the qth variation data a q For example, the specific formula is as follows: , Where z is a random number between [0, 1]; B5, mutation operation, randomly selects a variation data from the variation data group, and performs mutation operation according to a preset probability to obtain new variation data; B6, using the obtained optimal value to update the weight threshold, training to obtain the current change importance model.

[0032] In step S7, determining whether the important change is a continuous change specifically includes the following steps: C1, first, detect each sub-annotation data spectrum in the image annotation result based on the local model; C2, if the posterior score of a sub-annotated spectrogram exceeds the threshold, it is considered a local change; C3, the system records the start time and end time of continuous local changes. The start time is the left edge of the window corresponding to the first local change, and the end time is the right edge of the window corresponding to the last local change. C4, when continuous local changes occur, it indicates that there are continuous changes in the image annotations within the time period from the start time to the end time, that is, the important changes currently detected are continuous changes; C5, if no continuous local changes occur, the currently detected important changes are considered as discontinuous changes; C6, re-inputting the non-continuous change image annotations in step C5 into step C1 for subsequent operations until there is no continuous change in the important change annotations.

[0033] The video capture authentication in step S9 specifically includes the following steps: D1, in response to each video segment captured in step S8, extracting image data that meets the specified image condition from each video segment; any image data that meets the specified image condition carries at least identification information and timestamp information of the beacon device that sent the image data; D2, if extracted, obtaining the specified data in the extracted image data; wherein the specified data at least includes the identification information and timestamp information of the beacon device that sent the extracted image data; D3, obtaining target identification information of each video segment captured in step S8, and target timestamp information of the video; D4, performing matching analysis on the target timestamp information and the timestamp information in the specified data to obtain a first analysis result, and performing matching analysis on the target identification information and the identification information in the specified data to obtain a second analysis result; D5, based on the first analysis result and the second analysis result, determine the authentication result of the video; wherein the authentication result is used to indicate whether each video segment captured in step S8 is fake.

[0034] A target video capture device based on image screening comprises a user-side input module, a sampling interval setting module, a picture acquisition module, a screen segmentation module, a screen comparison module, a screen annotation module, a data judgment module, an importance degree calculation module, a data judgment module, a video capture module, a video capture authentication module, a user-side input module, a sampling interval setting module, a picture acquisition module, a screen segmentation module, a screen comparison module, a screen annotation module, a data judgment module, an importance degree calculation module, a data judgment module, a video capture module, and a video capture authentication module; The user-side input module is used for the user to input the image to be queried; The sampling interval setting module is used to set the sampling interval, which is the playback time period of the video; The image acquisition module is used to obtain images within the sampling interval; The screen segmentation module is used to perform screen segmentation on the image within the sampling interval and the standard image based on screen segmentation, and to segment the image within the sampling interval and the standard image into movable objects and fixed objects; The image comparison module is used to determine whether the movable object in the image screen within the sampling interval has changed relative to the movable object in the standard image; The image annotation module is used to annotate the changed image in the sampling interval as a changed image and annotate the changed position in the image in the sampling interval based on the image annotation model, and annotate the unchanged image in the sampling interval as an unchanged image; The data judgment module is used to judge the reason why the movable object in the image marked as a changed image changes based on the judgment model; The importance calculation module is used to calculate the importance of the current change based on the importance calculation model, and sort according to the importance, preset a detection threshold, retain the annotation for the change image whose importance of the current change is higher than the preset detection threshold, and delete the annotation for the change image whose importance of the current change is lower than the preset detection threshold; The continuous change judgment module is used to judge whether the change of the change image retained with the annotation in the importance calculation module is a continuous change; The video capture module is used to capture the video with complete annotation changes according to the changes of the corresponding annotations in the picture annotation module for the changing images whose results are judged as continuous changes in the data judgment module; The video capture authentication module performs video capture authentication on each video segment captured in the video capture module based on the video comparison authentication model.

[0035] An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions; The processor implements the target video capture method when executing the computer program instructions.

[0036] A computer storage medium, characterized in that computer program instructions are stored on the computer storage medium, and a target video capture method is implemented when the computer program instructions are executed by a processor.

[0037] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A target video capture method based on image screening, characterized in that: The following steps are involved: S1, the user inputs a query image through an input terminal, and the query image is used as a standard image; S2, set the sampling interval; S3, acquiring an image within the sampling interval, and performing screen segmentation on the image within the sampling interval and the standard image based on screen segmentation, and dividing the image within the sampling interval and the standard image into a movable object and a fixed object; S4, judging whether the movable object in the image frame within the sampling interval has changed relative to the movable object in the standard image based on the image comparison model, marking the changed image frame within the sampling interval as a changed image and marking the changed position within the image frame within the sampling interval based on the image annotation model, and marking the unchanged image frame within the sampling interval as an unchanged image; S5, judging the reason why the movable object in the changed image in step S4 changes based on the judgment model, wherein the reason for the change is not limited to human factors, environmental factors, and light factors; S6, calculating the importance of the current change based on the importance calculation model, and sorting according to the importance, presetting a detection threshold, retaining the annotation for the change image whose importance of the current change is higher than the preset detection threshold, and deleting the annotation for the change image whose importance of the current change is lower than the preset detection threshold; S7, determining whether the change of the marked change image retained in step S6 is a continuous change; S8, for the changing image judged as continuously changing in step S7, according to the change of the corresponding annotation in step S4, intercept the video with complete annotated changes, for the changing images with multiple annotated changes, intercept and classify and store according to different annotations, wherein the specific standard for video interception is a complete change process of the annotation of the changing image; S9, performing video capture authentication on each video segment captured in step S8 based on the video comparison authentication model.

2. The target video capture method based on image screening according to claim 1 is characterized in that: In step S3, the image within the sampling interval and the standard image are segmented based on the image segmentation, which specifically includes the following steps: A1, segment the image, mark one part of the image as fixed objects as N, and the other part as movable objects as M. Array F[n] is used to record the labels; A2, scan the fixed object and movable object area pixel by pixel, obtain the angle value x1 of each pixel in the color vector graph, and set the value of F[x1] to 1; A3, scan the M area containing the movable object point by point, obtain the angle value x2 corresponding to the non-pixel point, calculate F[x2], if the value of the point is not 1, retain it, otherwise remove it; The movable object photo is set as P, and P(x,y) is defined as the brightness value of the point with internal coordinates (x,y) on the movable object, which is converted into polar coordinates using the formula: Where P'(r,θ) represents the brightness change value of the point with internal coordinates (r,θ); In order to reduce the unknown influence of local features such as cracks of different degrees on movable objects on segmentation, we first perform Gaussian filtering on the image, then extract the brightness channel and hue channel from the three color attributes for polar coordinate transformation. After completion, we use the edge detection operator to perform an edge detection and use a vertical edge detector to detect the image; The detection result is converted back to the rectangular coordinate system using the following formula, and then a morphological operation is performed to obtain a closed edge: By identifying different closed edges, the specific shape of movable objects can be detected, which facilitates image segmentation.

3. The target video capture method based on image screening according to claim 1 is characterized in that: The generation of the importance model in step S6 specifically includes the following steps: B1, random initialization variation data set; B2, calculate the data fitness, based on the weight value and initial threshold in the back propagation neural network determined by the individual, the specific calculation formula is as follows: ， Where a is the number of nodes of the variable data, y b is the prediction data of the bth node, o b is the actual data of the bth node, and c is the coefficient; B3, selection operation, using the roulette method to perform the selection operation, and then select individuals with good fitness from the variation data to form a new variation data group. The specific formula for the probability of individual selection is as follows: ； Where c is the coefficient, N is a random number, and P b represents the data fitness of the b-th node, P j Represents the data fitness of the jth node; B4, cross operation, randomly select two change data for cross operation, so that the mth change data a at the jth position m and the qth variation data a q For example, the specific formula is as follows: ， Where z is a random number between [0, 1]; B5, mutation operation, randomly selects a variation data from the variation data group, and performs mutation operation according to a preset probability to obtain new variation data; B6, using the obtained optimal value to update the weight threshold, training to obtain the current change importance model.

4. The target video capture method based on image screening according to claim 1 is characterized in that: In step S7, determining whether the important change is a continuous change specifically includes the following steps: C1, first, detect each sub-annotation data spectrum in the image annotation result based on the local model; C2, if the posterior score of a sub-annotated spectrogram exceeds the threshold, it is considered a local change; C3, the system records the start time and end time of continuous local changes. The start time is the left edge of the window corresponding to the first local change, and the end time is the right edge of the window corresponding to the last local change. C4, when continuous local changes occur, it indicates that there are continuous changes in the image annotations within the time period from the start time to the end time, that is, the important changes currently detected are continuous changes; C5, if no continuous local changes occur, the currently detected important changes are considered as discontinuous changes; C6, re-inputting the non-continuous change image annotations in step C5 into step C1 for subsequent operations until there is no continuous change in the important change annotations.

5. The target video capture method based on image screening according to claim 4 is characterized in that: The video capture authentication in step S9 specifically includes the following steps: D1, in response to each video segment captured in step S8, extracting image data that meets the specified image condition from each video segment; any image data that meets the specified image condition carries at least identification information and timestamp information of the beacon device that sends the image data; D2, if extracted, obtaining the specified data in the extracted image data; wherein the specified data at least includes the identification information and timestamp information of the beacon device that sent the extracted image data; D3, obtaining target identification information of each video segment intercepted in step S8, and target timestamp information of the video; D4, performing a matching analysis on the target timestamp information and the timestamp information in the specified data to obtain a first analysis result, and performing a matching analysis on the target identification information and the identification information in the specified data to obtain a second analysis result; D5, based on the first analysis result and the second analysis result, determining the authentication result of the video; wherein the authentication result is used to indicate whether each video segment captured in the step S8 is fake.

6. A target video capture device based on image screening, which is applicable to the target video capture method based on image screening according to any one of claims 1 to 5, characterized in that: It includes a user-side input module, a sampling interval setting module, a picture acquisition module, a picture segmentation module, a picture comparison module, a picture annotation module, a data judgment module, an importance calculation module, a continuous change judgment module, a video capture module, and a video capture authentication module; The user-side input module is used for the user to input the image to be queried; The sampling interval setting module is used to set the sampling interval, and the sampling interval is the playing time period of the video; The image acquisition module is used to acquire images within a sampling interval; The screen segmentation module is used to perform screen segmentation on the image within the sampling interval and the standard image based on screen segmentation, and to segment the image within the sampling interval and the standard image into movable objects and fixed objects; The image comparison module is used to determine whether the movable object in the image frame within the sampling interval has changed relative to the movable object in the standard image; The picture annotation module is used to annotate the changed image pictures in the sampling interval as changed images and annotate the changed positions in the image pictures in the sampling interval based on the picture annotation model, and annotate the unchanged image pictures in the sampling interval as unchanged images; The data judgment module is used to judge the reason why the movable object in the image marked as a changed image changes based on the judgment model; The importance calculation module is used to calculate the importance of the current change based on the importance calculation model, and sort according to the importance, preset a detection threshold, retain the annotation for the change image whose importance of the current change is higher than the preset detection threshold, and delete the annotation for the change image whose importance of the current change is lower than the preset detection threshold; The continuous change judgment module is used to judge whether the change of the change image with the mark retained in the importance calculation module is a continuous change; The video capture module is used to capture the video with complete annotated changes according to the changes of the corresponding annotations in the picture annotation module for the changing images judged as continuously changing by the data judgment module; The video capture authentication module performs video capture authentication on each video segment captured in the video capture module based on a video comparison authentication model.

7. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the target video capture method according to any one of claims 1 to 5 is implemented.

8. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the target video capture method according to any one of claims 1 to 5 is implemented.

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