A vehicle sampling abnormality warning method and system based on video monitoring

Abstract

The present invention relates to the technical field of vehicle early warning, and discloses a vehicle sampling abnormality early warning method and system based on video monitoring, which comprises the following steps: obtaining original video monitoring data corresponding to a vehicle to be warned of abnormality, determining a target monitoring video to be analyzed; dividing the target monitoring video to be analyzed into multiple sub-monitoring videos, and calculating a sub-monitoring video analysis coefficient for each sub-monitoring video; obtaining a safety target monitoring video corresponding to the target monitoring video to be analyzed, and determining a corresponding sub-safety video analysis coefficient; classifying and processing the sub-monitoring video analysis coefficients according to the sub-safety video analysis coefficients, and calculating a vehicle abnormality early warning analysis coefficient; judging whether the vehicle to be warned of abnormality has abnormality based on the vehicle abnormality early warning analysis coefficient, and issuing an abnormality early warning when the vehicle to be warned of abnormality has abnormality, thereby realizing accurate identification and rapid early warning of vehicle behavior, ensuring the safety and reliability of the vehicle, and having important significance for the level of vehicle safety management.

Description

Technical Field

[0001] The present invention relates to the field of vehicle early warning technology, and in particular to a vehicle sampling abnormality early warning method and system based on video monitoring. Background Art

[0002] A video surveillance system, or VSCS (video surveillance and control system), is a monitoring system composed of cameras, transmission systems, and display devices, used for security purposes. A video surveillance system primarily consists of front-end equipment, transmission systems, storage devices, control components, and display units. Front-end equipment includes various types of cameras, such as gun-type, dome cameras, and high-speed dome cameras. As a crucial component of modern security technology, the development and application of video surveillance systems are crucial for improving social security and optimizing resource allocation. In the future, with continued technological innovation, video surveillance systems will play an even more critical role in smart city development and public safety.

[0003] Traditional traffic monitoring systems rely heavily on manual surveillance or simple gaze detection equipment, which is not only inefficient but also struggles to provide comprehensive monitoring and timely warnings in complex scenarios. In recent years, with the advancement of deep learning technology and high-performance computing platforms, vehicle warning methods based on video surveillance have become a research hotspot. Summary of the Invention

[0004] The embodiments of the present invention provide a vehicle sampling anomaly warning method and system based on video surveillance. By optimizing algorithms and enhancing data processing capabilities, it achieves accurate identification and rapid warning of vehicle behavior, ensuring the safety and reliability of the vehicle, which is of great significance to the level of vehicle safety management.

[0005] To achieve the above objectives, the present invention provides a vehicle sampling abnormality warning method based on video monitoring, comprising:

[0006] Determine a vehicle to be warned of an abnormality, obtain raw video surveillance data corresponding to the vehicle to be warned of an abnormality, and process the raw video surveillance data to determine a target surveillance video to be analyzed;

[0007] Dividing the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyzing the sub-surveillance videos, and calculating a sub-surveillance video analysis coefficient for each sub-surveillance video based on the analysis results;

[0008] Obtaining a security target surveillance video corresponding to the target surveillance video to be analyzed, and analyzing the security target surveillance video to determine a corresponding sub-security video analysis coefficient;

[0009] Classifying the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculating the vehicle abnormality warning analysis coefficient of the vehicle to be warned of abnormality based on the classification processing result;

[0010] Based on the vehicle abnormality warning analysis coefficient, it is determined whether the vehicle to be warned of abnormality has abnormality, and when the vehicle to be warned of abnormality has abnormality, an abnormality warning is issued.

[0011] Furthermore, when determining a vehicle to be warned of abnormality, obtaining original video surveillance data corresponding to the vehicle to be warned of abnormality, and processing the original video surveillance data to determine the target surveillance video to be analyzed, the method includes:

[0012] Preprocessing the raw video surveillance data, wherein the preprocessing includes denoising, contrast enhancement, and brightness adjustment;

[0013] Segmenting the pre-processed raw video surveillance data into a plurality of video regions using an image segmentation algorithm, wherein the image segmentation algorithm includes any one or more combinations of a threshold segmentation method, an edge detection-based segmentation method, a region-based segmentation method, or a deep learning-based segmentation method;

[0014] Determining a target video area to be extracted from a plurality of video areas according to a preset extraction rule, wherein the preset extraction rule includes shape, size, position, color feature, texture feature or motion feature;

[0015] Extracting a target video segment from the target video area, and performing post-processing on the extracted target video segment, wherein the post-processing includes smoothing edges and adjusting color balance;

[0016] The processed target video clip is output as the target surveillance video to be analyzed.

[0017] Furthermore, when dividing the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyzing the sub-surveillance videos, and calculating the sub-surveillance video analysis coefficient of each sub-surveillance video based on the analysis results, the method includes:

[0018] Extracting a video frame image from the sub-surveillance video and extracting corresponding video pixels;

[0019] Clustering all video pixels to determine video pixel clusters, and determining cluster centers of the video pixel clusters;

[0020] Determine the video distance from each video pixel to the cluster center and construct a video distance set;

[0021] Calculating the mean and standard deviation of the video distance set;

[0022] For all video distances greater than the mean and greater than the standard deviation, generate a video distance large flag;

[0023] For all video distances that are equal to the mean and the standard deviation, generate video distance isolabels;

[0024] Generate video distance difference identifiers for all remaining video distances;

[0025] Count the number of large identifiers of the video distance large identifier, count the number of equal identifiers of the video distance equal identifiers, and count the number of difference identifiers of the video distance difference identifiers;

[0026] The sub-surveillance video analysis coefficient of each sub-surveillance video is calculated based on the number of large identifiers, the number of equal identifiers, and the number of difference identifiers.

[0027] Furthermore, when classifying the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients and calculating the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned based on the classification processing results, the method includes:

[0028] Compare all sub-surveillance video analysis coefficients with the corresponding sub-safety video analysis coefficients one by one, divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficients into a lower difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0029] Dividing all sub-surveillance video coefficients equal to the sub-security video analysis coefficients into a set of equal difference coefficients;

[0030] Divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficient into an upper difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0031] Calculating a vehicle abnormality warning analysis coefficient of the vehicle to be warned according to the lower difference coefficient set, the equal difference coefficient set, and the upper difference coefficient set;

[0032] ;

[0033] ;

[0034] ;

[0035] Among them, w is the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned, a is the adjustment coefficient of the vehicle abnormality warning analysis coefficient, e(g, k) is the comparison function between the upper difference coefficient set and the lower difference coefficient set, Δd is the weight corresponding to the sub-monitoring video analysis coefficient, and the value is [0.85, 1.25], f is the number of sub-monitoring video analysis coefficients in the upper difference coefficient set, β is the loss factor for calculating the vehicle abnormality warning analysis coefficient, and Δt i is the corresponding difference between the neutron surveillance video analysis coefficient of the upper difference coefficient set and the neutron surveillance video analysis coefficient of the lower difference coefficient set, max (Δt i ) is the difference Δt from all i The maximum value obtained in Δr 均 For all differences Δt i The average value of t i is the i-th sub-surveillance video analysis coefficient in the above difference coefficient set, r i is the sub-surveillance video analysis coefficient corresponding to the i-th sub-surveillance video analysis coefficient in the lower difference coefficient set;

[0036] The adjustment coefficient a of the vehicle abnormality warning analysis coefficient is determined according to the following method:

[0037] Counting the equal number of sub-monitoring video analysis coefficients in the equal difference coefficient set;

[0038] Counting the sum Q of the sub-monitoring video analysis coefficients in the upper anomaly coefficient set and the lower difference coefficient set;

[0039] Presetting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient;

[0040] When the equal number is less than 0.85Q, the first preset adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient;

[0041] When the equal quantity is greater than or equal to 0.85Q and less than 1.25Q, the second adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient;

[0042] When the equal number is greater than 1.25Q, the third adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient.

[0043] Furthermore, when judging whether the vehicle to be warned has an abnormality based on the vehicle abnormality warning analysis coefficient, the method includes:

[0044] Obtaining a preset vehicle abnormality warning analysis coefficient, and determining whether the vehicle to be warned has an abnormality based on a relationship between the abnormality warning analysis coefficient and the preset abnormality warning analysis coefficient;

[0045] When the abnormality warning analysis coefficient is less than the preset abnormality warning analysis coefficient, it is determined that the vehicle to be warned has an abnormality;

[0046] When the abnormality warning analysis coefficient is greater than or equal to the preset abnormality warning analysis coefficient, it is determined that there is no abnormality in the vehicle to be warned.

[0047] In order to achieve the above object, the present invention also provides a vehicle sampling abnormality warning system based on video monitoring, comprising:

[0048] A video processing module is used to determine a vehicle to be warned of an abnormality, obtain raw video surveillance data corresponding to the vehicle to be warned of an abnormality, and process the raw video surveillance data to determine a target surveillance video to be analyzed;

[0049] a first calculation module, configured to divide the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyze the sub-surveillance videos, and calculate a sub-surveillance video analysis coefficient for each sub-surveillance video based on the analysis result;

[0050] a video analysis module, configured to obtain a security target surveillance video corresponding to the target surveillance video to be analyzed, and analyze the security target surveillance video to determine a corresponding sub-security video analysis coefficient;

[0051] a second calculation module, configured to classify the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculate the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned based on the classification processing result;

[0052] The abnormality warning module is used to determine whether the vehicle to be warned has an abnormality based on the vehicle abnormality warning analysis coefficient, and to issue an abnormality warning when the vehicle to be warned has an abnormality.

[0053] Furthermore, the video processing module is used to:

[0054] The video processing module is used to pre-process the raw video surveillance data, wherein the pre-processing includes denoising, contrast enhancement, and brightness adjustment;

[0055] The video processing module is used to segment the pre-processed raw video surveillance data into multiple video regions using an image segmentation algorithm, wherein the image segmentation algorithm includes any one or more combinations of a threshold segmentation method, an edge detection-based segmentation method, a region-based segmentation method, or a deep learning-based segmentation method;

[0056] The video processing module is used to determine a target video area to be extracted from multiple video areas according to a preset extraction rule, wherein the preset extraction rule includes shape, size, position, color feature, texture feature or motion feature;

[0057] The video processing module is used to extract a target video segment from the target video area and perform post-processing on the extracted target video segment, wherein the post-processing includes smoothing edges and adjusting color balance;

[0058] The video processing module is used to output the post-processed target video segment as the target monitoring video to be analyzed.

[0059] Furthermore, the first calculation module is used for:

[0060] The first calculation module is used to extract a video frame image from the sub-surveillance video and extract corresponding video pixels;

[0061] The first calculation module is used to cluster all video pixels, determine video pixel clusters, and determine the cluster centers of the video pixel clusters;

[0062] The first calculation module is used to determine the video distance from each video pixel to the cluster center and construct a video distance set;

[0063] The first calculation module is used to calculate the mean and standard deviation of the video distance set;

[0064] The first calculation module is used to generate a video distance large flag for all video distances greater than the mean and greater than the standard deviation;

[0065] The first calculation module is used to generate video distance and other identifiers for all video distances that are equal to the mean and the standard deviation;

[0066] The first calculation module is used to generate a video distance difference identifier for all remaining video distances;

[0067] The first calculation module is used to count the number of large identifiers of the video distance large identifier, count the number of equal identifiers of the video distance equal identifier, and count the number of difference identifiers of the video distance difference identifier;

[0068] The first calculation module is used to calculate the sub-surveillance video analysis coefficient of each sub-surveillance video based on the number of large identifiers, the number of equal identifiers, and the number of difference identifiers.

[0069] Furthermore, the second calculation module is used for:

[0070] The second calculation module is used to compare all sub-surveillance video analysis coefficients with corresponding sub-safety video analysis coefficients one by one, divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficients into a lower difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0071] The second calculation module is used to divide all sub-surveillance video coefficients equal to the sub-security video analysis coefficients into a set of equal difference coefficients;

[0072] The second calculation module is used to divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficient into an upper difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0073] The second calculation module is used to calculate the vehicle abnormality warning analysis coefficient of the vehicle to be warned based on the lower difference coefficient set, the equal difference coefficient set and the upper difference coefficient set;

[0074] ;

[0075] ;

[0076] ;

[0077] Among them, w is the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned, a is the adjustment coefficient of the vehicle abnormality warning analysis coefficient, e(g, k) is the comparison function between the upper difference coefficient set and the lower difference coefficient set, Δd is the weight corresponding to the sub-monitoring video analysis coefficient, and the value is [0.85, 1.25], f is the number of sub-monitoring video analysis coefficients in the upper difference coefficient set, β is the loss factor for calculating the vehicle abnormality warning analysis coefficient, and Δt i is the corresponding difference between the neutron surveillance video analysis coefficient of the upper difference coefficient set and the neutron surveillance video analysis coefficient of the lower difference coefficient set, max (Δt i ) is the difference Δt from all i The maximum value obtained in Δr 均 For all differences Δt i The average value of t i is the i-th sub-surveillance video analysis coefficient in the above difference coefficient set, r i is the sub-surveillance video analysis coefficient corresponding to the i-th sub-surveillance video analysis coefficient in the lower difference coefficient set;

[0078] The second calculation module is used to determine the adjustment coefficient a of the vehicle abnormality warning analysis coefficient according to the following method:

[0079] The second calculation module is used to count the equal number of sub-monitoring video analysis coefficients in the equal difference coefficient set;

[0080] The second calculation module is used to calculate the sum Q of the sub-monitoring video analysis coefficients in the upper anomaly coefficient set and the lower difference coefficient set;

[0081] The second calculation module is used to pre-set a first preset adjustment coefficient, a second preset adjustment coefficient and a third preset adjustment coefficient;

[0082] The second calculation module is configured to use the first preset adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal number is less than 0.85Q;

[0083] The second calculation module is configured to use the second adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal quantity is greater than or equal to 0.85Q and less than 1.25Q;

[0084] The second calculation module is configured to use the third adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal number is greater than 1.25Q.

[0085] Furthermore, the abnormal warning module is used to:

[0086] The abnormality warning module is used to obtain a preset vehicle abnormality warning analysis coefficient, and determine whether the vehicle to be warned has an abnormality based on the relationship between the abnormality warning analysis coefficient and the preset abnormality warning analysis coefficient;

[0087] The abnormality warning module is configured to determine that an abnormality exists in the vehicle to be warned when the abnormality warning analysis coefficient is less than the preset abnormality warning analysis coefficient;

[0088] The abnormality warning module is used to determine that there is no abnormality in the vehicle to be warned when the abnormality warning analysis coefficient is greater than or equal to the preset abnormality warning analysis coefficient.

[0089] Compared with the prior art, the present invention has the following beneficial effects:

[0090] The present invention discloses a vehicle sampling abnormality warning method and system based on video surveillance, which obtains original video surveillance data corresponding to a vehicle to be warned of abnormality, determines a target surveillance video to be analyzed; divides the target surveillance video to be analyzed into multiple sub-surveillance videos, and calculates a sub-surveillance video analysis coefficient for each sub-surveillance video; obtains a safety target surveillance video corresponding to the target surveillance video to be analyzed, and determines a corresponding sub-safety video analysis coefficient; classifies the sub-surveillance video analysis coefficient according to the sub-safety video analysis coefficient, and calculates a vehicle abnormality warning analysis coefficient; determines whether the vehicle to be warned of abnormality has abnormality based on the vehicle abnormality warning analysis coefficient, and issues an abnormality warning when the vehicle to be warned of abnormality has abnormality, thereby realizing accurate identification and rapid warning of vehicle behavior, ensuring the safety and reliability of the vehicle, and having important significance for the level of vehicle safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0092] Figure 1 A schematic diagram of a process for warning abnormal vehicle sampling based on video monitoring in an embodiment of the present invention is shown;

[0093] Figure 2 The figure shows a schematic structural diagram of a vehicle sampling abnormality warning system based on video monitoring in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0095] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0096] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0097] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0098] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0099] like Figure 1 As shown, an embodiment of the present invention discloses a vehicle sampling abnormality warning method based on video monitoring, comprising:

[0100] S110: Determine a vehicle to be warned of an abnormality, obtain original video surveillance data corresponding to the vehicle to be warned of an abnormality, and process the original video surveillance data to determine a target surveillance video to be analyzed;

[0101] In some embodiments of the present application, when determining a vehicle to be warned of an abnormality, obtaining raw video surveillance data corresponding to the vehicle to be warned of an abnormality, and processing the raw video surveillance data to determine a target surveillance video to be analyzed, the process includes:

[0102] Preprocessing the raw video surveillance data, wherein the preprocessing includes denoising, contrast enhancement, and brightness adjustment;

[0103] Segmenting the pre-processed raw video surveillance data into a plurality of video regions using an image segmentation algorithm, wherein the image segmentation algorithm includes any one or more combinations of a threshold segmentation method, an edge detection-based segmentation method, a region-based segmentation method, or a deep learning-based segmentation method;

[0104] Determining a target video area to be extracted from a plurality of video areas according to a preset extraction rule, wherein the preset extraction rule includes shape, size, position, color feature, texture feature or motion feature;

[0105] Extracting a target video segment from the target video area, and performing post-processing on the extracted target video segment, wherein the post-processing includes smoothing edges and adjusting color balance;

[0106] The processed target video clip is output as the target surveillance video to be analyzed.

[0107] The beneficial effect of the above technical solution is that the present invention obtains the target monitoring video to be analyzed by performing a series of processing on the original video monitoring data, which can remove invalid information, improve vehicle warning efficiency, and lay the foundation for vehicle warning.

[0108] S120: Divide the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyze the sub-surveillance videos, and calculate a sub-surveillance video analysis coefficient for each sub-surveillance video based on the analysis results;

[0109] In some embodiments of the present application, when dividing the target surveillance video to be analyzed into multiple sub-surveillance videos, analyzing the sub-surveillance videos, and calculating the sub-surveillance video analysis coefficient of each sub-surveillance video based on the analysis results, the following steps are included:

[0110] Extracting a video frame image from the sub-surveillance video and extracting corresponding video pixels;

[0111] Clustering all video pixels to determine video pixel clusters, and determining cluster centers of the video pixel clusters;

[0112] Determine the video distance from each video pixel to the cluster center and construct a video distance set;

[0113] Calculating the mean and standard deviation of the video distance set;

[0114] For all video distances greater than the mean and greater than the standard deviation, generate a video distance large flag;

[0115] For all video distances that are equal to the mean and the standard deviation, generate video distance isolabels;

[0116] Generate video distance difference identifiers for all remaining video distances;

[0117] Count the number of large identifiers of the video distance large identifier, count the number of equal identifiers of the video distance equal identifiers, and count the number of difference identifiers of the video distance difference identifiers;

[0118] The sub-surveillance video analysis coefficient of each sub-surveillance video is calculated based on the number of large identifiers, the number of equal identifiers, and the number of difference identifiers.

[0119] In this embodiment, each frame corresponds to a corresponding image, that is, a video frame image.

[0120] In this embodiment, the method for determining the cluster center is complex and mature, and will not be introduced in detail here.

[0121] In this embodiment, when calculating the sub-surveillance video analysis coefficient of each sub-surveillance video based on the number of large identifiers, the number of equal identifiers, and the number of different identifiers, the sub-surveillance video analysis coefficient is calculated according to the following formula:

[0122] ;

[0123] Among them, p is the sub-surveillance video analysis coefficient, c1 is the number of equal identifications, c2 is the number of large identifications, and c3 is the number of difference identifications.

[0124] The beneficial effect of the above technical solution is that the present invention can calculate the sub-surveillance video analysis coefficient of each sub-surveillance video based on the number of large identifiers, the number of equal identifiers and the number of difference identifiers, thereby ensuring the calculation precision and accuracy of the sub-surveillance video analysis coefficient and avoiding the calculation errors caused by manual participation.

[0125] S130: Acquire a security target surveillance video that is similar to the target surveillance video to be analyzed, analyze the security target surveillance video, and determine a corresponding sub-security video analysis coefficient;

[0126] In this embodiment, the safety target monitoring video refers to the monitoring video collected when there is no abnormality in the vehicle.

[0127] In this embodiment, the sub-security video analysis coefficient of the security target monitoring video is calculated based on the above method. To save space, it will not be repeated here.

[0128] S140: Classifying the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculating the vehicle abnormality warning analysis coefficient of the vehicle to be abnormality warned based on the classification processing result;

[0129] In some embodiments of the present application, when classifying the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculating the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned based on the classification processing results, the method includes:

[0130] Compare all sub-surveillance video analysis coefficients with the corresponding sub-safety video analysis coefficients one by one, divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficients into a lower difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0131] Dividing all sub-surveillance video coefficients equal to the sub-security video analysis coefficients into a set of equal difference coefficients;

[0132] Divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficient into an upper difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0133] Calculating a vehicle abnormality warning analysis coefficient of the vehicle to be warned according to the lower difference coefficient set, the equal difference coefficient set, and the upper difference coefficient set;

[0134] ;

[0135] ;

[0136] ;

[0137] Among them, w is the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned, a is the adjustment coefficient of the vehicle abnormality warning analysis coefficient, e(g, k) is the comparison function between the upper difference coefficient set and the lower difference coefficient set, Δd is the weight corresponding to the sub-monitoring video analysis coefficient, and the value is [0.85, 1.25], f is the number of sub-monitoring video analysis coefficients in the upper difference coefficient set, β is the loss factor for calculating the vehicle abnormality warning analysis coefficient, and Δt i is the corresponding difference between the neutron surveillance video analysis coefficient of the upper difference coefficient set and the neutron surveillance video analysis coefficient of the lower difference coefficient set, max (Δt i ) is the difference Δt from all i The maximum value obtained in Δr 均 For all differences Δt i The average value of t i is the i-th sub-surveillance video analysis coefficient in the above difference coefficient set, r i is the sub-surveillance video analysis coefficient corresponding to the i-th sub-surveillance video analysis coefficient in the lower difference coefficient set;

[0138] The adjustment coefficient a of the vehicle abnormality warning analysis coefficient is determined according to the following method:

[0139] Counting the equal number of sub-monitoring video analysis coefficients in the equal difference coefficient set;

[0140] Counting the sum Q of the sub-monitoring video analysis coefficients in the upper anomaly coefficient set and the lower difference coefficient set;

[0141] Presetting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient;

[0142] When the equal number is less than 0.85Q, the first preset adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient;

[0143] When the equal quantity is greater than or equal to 0.85Q and less than 1.25Q, the second adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient;

[0144] When the equal number is greater than 1.25Q, the third adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient.

[0145] In this embodiment, the first preset adjustment coefficient is 0.85, the second preset adjustment coefficient is 1, and the third preset adjustment coefficient is 1.25, which can be adjusted according to actual conditions.

[0146] The beneficial effect of the above technical solution is: the present invention calculates the vehicle abnormal warning analysis coefficient of the vehicle to be warned according to the lower difference coefficient set, the equal difference coefficient set and the upper difference coefficient set, thereby ensuring the calculation accuracy of the vehicle abnormal warning analysis coefficient, providing reliable data support and basis for vehicle warning analysis, and avoiding misjudgment. At the same time, the present application also adjusts the vehicle abnormal warning analysis coefficient according to the preset adjustment coefficient, realizes the dynamic adjustment of the vehicle abnormal warning analysis coefficient, and makes the calculation of the vehicle abnormal warning analysis coefficient more comprehensive.

[0147] S150: Determine whether the vehicle to be warned of abnormality has abnormality based on the vehicle abnormality warning analysis coefficient, and issue an abnormality warning when the vehicle to be warned of abnormality has abnormality.

[0148] In some embodiments of the present application, when determining whether the vehicle to be warned of abnormality has an abnormality based on the vehicle abnormality warning analysis coefficient, the method includes:

[0149] Obtaining a preset vehicle abnormality warning analysis coefficient, and judging whether the vehicle to be warned has an abnormality based on a relationship between the abnormality warning analysis coefficient and the preset abnormality warning analysis coefficient;

[0150] When the abnormality warning analysis coefficient is less than the preset abnormality warning analysis coefficient, it is determined that the vehicle to be warned has an abnormality;

[0151] When the abnormality warning analysis coefficient is greater than or equal to the preset abnormality warning analysis coefficient, it is determined that there is no abnormality in the vehicle to be warned.

[0152] The beneficial effect of the above technical solution is: the present invention judges whether there is an abnormality in the vehicle to be warned based on the relationship between the abnormal warning analysis coefficient and the preset abnormal warning analysis coefficient, thereby realizing accurate identification and rapid warning of vehicle behavior, ensuring the safety and reliability of the vehicle, and is of great significance to the level of vehicle safety management.

[0153] In order to further illustrate the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0154] Correspondingly, such as Figure 2 As shown, the present application also provides a vehicle sampling abnormality warning system based on video monitoring, including:

[0155] A video processing module is used to determine a vehicle to be warned of an abnormality, obtain raw video surveillance data corresponding to the vehicle to be warned of an abnormality, and process the raw video surveillance data to determine a target surveillance video to be analyzed;

[0156] a first calculation module, configured to divide the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyze the sub-surveillance videos, and calculate a sub-surveillance video analysis coefficient for each sub-surveillance video based on the analysis result;

[0157] a video analysis module, configured to obtain a security target surveillance video corresponding to the target surveillance video to be analyzed, and analyze the security target surveillance video to determine a corresponding sub-security video analysis coefficient;

[0158] a second calculation module, configured to classify the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculate the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned based on the classification processing result;

[0159] The abnormality warning module is used to determine whether the vehicle to be warned has an abnormality based on the vehicle abnormality warning analysis coefficient, and to issue an abnormality warning when the vehicle to be warned has an abnormality.

[0160] In some embodiments of the present application, the video processing module is used to:

[0161] The video processing module is used to pre-process the raw video surveillance data, wherein the pre-processing includes denoising, contrast enhancement, and brightness adjustment;

[0162] The video processing module is used to segment the pre-processed raw video surveillance data into multiple video regions using an image segmentation algorithm, wherein the image segmentation algorithm includes any one or more combinations of a threshold segmentation method, an edge detection-based segmentation method, a region-based segmentation method, or a deep learning-based segmentation method;

[0163] The video processing module is used to determine a target video area to be extracted from multiple video areas according to a preset extraction rule, wherein the preset extraction rule includes shape, size, position, color feature, texture feature or motion feature;

[0164] The video processing module is used to extract a target video segment from the target video area and perform post-processing on the extracted target video segment, wherein the post-processing includes smoothing edges and adjusting color balance;

[0165] The video processing module is used to output the post-processed target video segment as the target monitoring video to be analyzed.

[0166] In some embodiments of the present application, the first computing module is configured to:

[0167] The first calculation module is used to extract a video frame image from the sub-surveillance video and extract corresponding video pixels;

[0168] The first calculation module is used to cluster all video pixels, determine video pixel clusters, and determine the cluster centers of the video pixel clusters;

[0169] The first calculation module is used to determine the video distance from each video pixel to the cluster center and construct a video distance set;

[0170] The first calculation module is used to calculate the mean and standard deviation of the video distance set;

[0171] The first calculation module is used to generate a video distance large flag for all video distances greater than the mean and greater than the standard deviation;

[0172] The first calculation module is used to generate video distance and other identifiers for all video distances that are equal to the mean and the standard deviation;

[0173] The first calculation module is used to generate a video distance difference identifier for all remaining video distances;

[0174] The first calculation module is used to count the number of large identifiers of the video distance large identifier, count the number of equal identifiers of the video distance equal identifier, and count the number of difference identifiers of the video distance difference identifier;

[0175] The first calculation module is used to calculate the sub-surveillance video analysis coefficient of each sub-surveillance video based on the number of large identifiers, the number of equal identifiers, and the number of difference identifiers.

[0176] In some embodiments of the present application, the second computing module is configured to:

[0177] The second calculation module is used to compare all sub-surveillance video analysis coefficients with corresponding sub-safety video analysis coefficients one by one, divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficients into a lower difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0178] The second calculation module is used to divide all sub-surveillance video coefficients equal to the sub-security video analysis coefficients into a set of equal difference coefficients;

[0179] The second calculation module is used to divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficient into an upper difference coefficient set, and sort the lower difference coefficient set by numerical value;

[0180] The second calculation module is used to calculate the vehicle abnormality warning analysis coefficient of the vehicle to be warned based on the lower difference coefficient set, the equal difference coefficient set and the upper difference coefficient set;

[0181] ;

[0182] ;

[0183] ;

[0184] Among them, w is the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned, a is the adjustment coefficient of the vehicle abnormality warning analysis coefficient, e(g, k) is the comparison function between the upper difference coefficient set and the lower difference coefficient set, Δd is the weight corresponding to the sub-monitoring video analysis coefficient, and the value is [0.85, 1.25], f is the number of sub-monitoring video analysis coefficients in the upper difference coefficient set, β is the loss factor for calculating the vehicle abnormality warning analysis coefficient, and Δt i is the corresponding difference between the neutron surveillance video analysis coefficient of the upper difference coefficient set and the neutron surveillance video analysis coefficient of the lower difference coefficient set, max (Δt i ) is the sum of all the differences Δt i The maximum value obtained in Δr 均 For all differences Δt i The average value of t i is the i-th sub-surveillance video analysis coefficient in the above difference coefficient set, r i is the sub-surveillance video analysis coefficient corresponding to the i-th sub-surveillance video analysis coefficient in the lower difference coefficient set;

[0185] The second calculation module is used to determine the adjustment coefficient a of the vehicle abnormality warning analysis coefficient according to the following method:

[0186] The second calculation module is used to count the equal number of sub-monitoring video analysis coefficients in the equal difference coefficient set;

[0187] The second calculation module is used to calculate the sum Q of the sub-monitoring video analysis coefficients in the upper anomaly coefficient set and the lower difference coefficient set;

[0188] The second calculation module is used to pre-set a first preset adjustment coefficient, a second preset adjustment coefficient and a third preset adjustment coefficient;

[0189] The second calculation module is configured to use the first preset adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal number is less than 0.85Q;

[0190] The second calculation module is configured to use the second adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal quantity is greater than or equal to 0.85Q and less than 1.25Q;

[0191] The second calculation module is configured to use the third adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal number is greater than 1.25Q.

[0192] In some embodiments of the present application, the abnormality warning module is used to:

[0193] The abnormality warning module is used to obtain a preset vehicle abnormality warning analysis coefficient, and determine whether the vehicle to be warned has an abnormality based on the relationship between the abnormality warning analysis coefficient and the preset abnormality warning analysis coefficient;

[0194] The abnormality warning module is configured to determine that an abnormality exists in the vehicle to be warned when the abnormality warning analysis coefficient is less than the preset abnormality warning analysis coefficient;

[0195] The abnormality warning module is used to determine that there is no abnormality in the vehicle to be warned when the abnormality warning analysis coefficient is greater than or equal to the preset abnormality warning analysis coefficient.

[0196] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0197] While the present invention has been described above with reference to exemplary embodiments, various modifications may be made and equivalent components may be substituted without departing from the scope of the present invention. In particular, the various features of the disclosed embodiments may be combined with one another in any manner, provided no structural conflicts exist. These combinations are not fully described in this specification for reasons of space and resource conservation.

[0198] Those skilled in the art will understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will still be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A vehicle sampling abnormality warning method based on video monitoring, characterized in that: include: Determine a vehicle to be warned of an abnormality, obtain original video surveillance data corresponding to the vehicle to be warned of an abnormality, and process the original video surveillance data to determine a target surveillance video to be analyzed; Dividing the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyzing the sub-surveillance videos, and calculating a sub-surveillance video analysis coefficient for each sub-surveillance video based on the analysis results; Obtaining a security target surveillance video corresponding to the target surveillance video to be analyzed, and analyzing the security target surveillance video to determine a corresponding sub-security video analysis coefficient; Classifying the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculating the vehicle abnormality warning analysis coefficient of the vehicle to be warned of abnormality based on the classification processing result; Based on the vehicle abnormality warning analysis coefficient, it is determined whether the vehicle to be warned of abnormality has abnormality, and when the vehicle to be warned of abnormality has abnormality, an abnormality warning is issued.

2. The vehicle sampling abnormality early warning method based on video monitoring according to claim 1 is characterized in that: When determining a vehicle to be warned of an abnormality, obtaining original video surveillance data corresponding to the vehicle to be warned of an abnormality, and processing the original video surveillance data to determine a target surveillance video to be analyzed, the method includes: Preprocessing the raw video surveillance data, wherein the preprocessing includes denoising, contrast enhancement, and brightness adjustment; Segmenting the pre-processed raw video surveillance data into a plurality of video regions using an image segmentation algorithm, wherein the image segmentation algorithm includes any one or more combinations of a threshold segmentation method, an edge detection-based segmentation method, a region-based segmentation method, or a deep learning-based segmentation method; Determining a target video area to be extracted from a plurality of video areas according to a preset extraction rule, wherein the preset extraction rule includes shape, size, position, color feature, texture feature or motion feature; Extracting a target video segment from the target video area, and performing post-processing on the extracted target video segment, wherein the post-processing includes smoothing edges and adjusting color balance; The processed target video clip is output as the target surveillance video to be analyzed.

3. The vehicle sampling abnormality early warning method based on video monitoring according to claim 1 is characterized in that: The target surveillance video to be analyzed is divided into a plurality of sub-surveillance videos, the sub-surveillance videos are analyzed, and a sub-surveillance video analysis coefficient of each sub-surveillance video is calculated based on the analysis result, including: Extracting a video frame image from the sub-surveillance video and extracting corresponding video pixels; Clustering all video pixels to determine video pixel clusters, and determining cluster centers of the video pixel clusters; Determine the video distance from each video pixel to the cluster center and construct a video distance set; Calculating the mean and standard deviation of the video distance set; For all video distances greater than the mean and greater than the standard deviation, generate a video distance large flag; For all video distances that are equal to the mean and the standard deviation, generate video distance isolabels; Generate video distance difference identifiers for all remaining video distances; Count the number of large identifiers of the video distance large identifier, count the number of equal identifiers of the video distance equal identifiers, and count the number of difference identifiers of the video distance difference identifiers; The sub-surveillance video analysis coefficient of each sub-surveillance video is calculated based on the number of large identifiers, the number of equal identifiers, and the number of difference identifiers.

4. The vehicle sampling abnormality early warning method based on video monitoring according to claim 1 is characterized in that: When classifying the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculating the vehicle abnormality warning analysis coefficient of the vehicle to be abnormality warned based on the classification processing result, the method includes: Compare all sub-surveillance video analysis coefficients with the corresponding sub-safety video analysis coefficients one by one, divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficients into a lower difference coefficient set, and sort the lower difference coefficient set by numerical value; Dividing all sub-surveillance video coefficients equal to the sub-security video analysis coefficients into a set of equal difference coefficients; Divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficient into an upper difference coefficient set, and sort the lower difference coefficient set by numerical value; Calculating a vehicle abnormality warning analysis coefficient of the vehicle to be warned according to the lower difference coefficient set, the equal difference coefficient set, and the upper difference coefficient set; ； ； ； Among them, w is the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned, a is the adjustment coefficient of the vehicle abnormality warning analysis coefficient, e(g, k) is the comparison function between the upper difference coefficient set and the lower difference coefficient set, Δd is the weight corresponding to the sub-monitoring video analysis coefficient, and the value is [0.85, 1.25], f is the number of sub-monitoring video analysis coefficients in the upper difference coefficient set, β is the loss factor for calculating the vehicle abnormality warning analysis coefficient, and Δt i is the corresponding difference between the neutron surveillance video analysis coefficient of the upper difference coefficient set and the neutron surveillance video analysis coefficient of the lower difference coefficient set, max (Δt i ) is the difference Δt from all i The maximum value obtained in Δr 均 For all differences Δt i The average value of t i is the i-th sub-surveillance video analysis coefficient in the above difference coefficient set, r i is the sub-surveillance video analysis coefficient corresponding to the i-th sub-surveillance video analysis coefficient in the lower difference coefficient set; The adjustment coefficient a of the vehicle abnormality warning analysis coefficient is determined according to the following method: Counting the equal number of sub-monitoring video analysis coefficients in the equal difference coefficient set; Counting the sum Q of the sub-monitoring video analysis coefficients in the upper difference coefficient set and the lower difference coefficient set; Presetting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the equal number is less than 0.85Q, the first preset adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient; When the equal number is greater than or equal to 0.85Q and less than 1.25Q, the second preset adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient; When the equal number is greater than 1.25Q, the third preset adjustment coefficient is used as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient.

5. The vehicle sampling abnormality early warning method based on video monitoring according to claim 1 is characterized in that: When judging whether the vehicle to be warned has an abnormality based on the vehicle abnormality warning analysis coefficient, the method includes: Obtaining a preset vehicle abnormality warning analysis coefficient, and judging whether the vehicle to be warned has an abnormality based on a relationship between the abnormality warning analysis coefficient and the preset abnormality warning analysis coefficient; When the abnormality warning analysis coefficient is less than the preset abnormality warning analysis coefficient, it is determined that the vehicle to be warned has an abnormality; When the abnormality warning analysis coefficient is greater than or equal to the preset abnormality warning analysis coefficient, it is determined that there is no abnormality in the vehicle to be warned.

6. A vehicle sampling abnormality warning system based on video monitoring, characterized in that: include: A video processing module is used to determine a vehicle to be warned of an abnormality, obtain raw video surveillance data corresponding to the vehicle to be warned of an abnormality, and process the raw video surveillance data to determine a target surveillance video to be analyzed; a first calculation module, configured to divide the target surveillance video to be analyzed into a plurality of sub-surveillance videos, analyze the sub-surveillance videos, and calculate a sub-surveillance video analysis coefficient for each sub-surveillance video based on the analysis result; a video analysis module, configured to obtain a security target surveillance video corresponding to the target surveillance video to be analyzed, and analyze the security target surveillance video to determine a corresponding sub-security video analysis coefficient; a second calculation module, configured to classify the corresponding sub-surveillance video analysis coefficients according to the sub-safety video analysis coefficients, and calculate the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned based on the classification processing result; The abnormality warning module is used to determine whether the vehicle to be warned has an abnormality based on the vehicle abnormality warning analysis coefficient, and to issue an abnormality warning when the vehicle to be warned has an abnormality.

7. The vehicle sampling abnormality warning system based on video monitoring according to claim 6 is characterized in that: The video processing module is used for: The video processing module is used to pre-process the raw video surveillance data, wherein the pre-processing includes denoising, contrast enhancement, and brightness adjustment; The video processing module is used to segment the pre-processed raw video surveillance data into multiple video regions using an image segmentation algorithm, wherein the image segmentation algorithm includes any one or more combinations of a threshold segmentation method, an edge detection-based segmentation method, a region-based segmentation method, or a deep learning-based segmentation method; The video processing module is used to determine a target video area to be extracted from multiple video areas according to a preset extraction rule, wherein the preset extraction rule includes shape, size, position, color feature, texture feature or motion feature; The video processing module is used to extract a target video segment from the target video area and perform post-processing on the extracted target video segment, wherein the post-processing includes smoothing edges and adjusting color balance; The video processing module is used to output the post-processed target video segment as the target monitoring video to be analyzed.

8. The vehicle sampling abnormality warning system based on video monitoring according to claim 6 is characterized in that: The first calculation module is used for: The first calculation module is used to extract a video frame image from the sub-surveillance video and extract corresponding video pixels; The first calculation module is used to cluster all video pixels, determine video pixel clusters, and determine the cluster centers of the video pixel clusters; The first calculation module is used to determine the video distance from each video pixel to the cluster center and construct a video distance set; The first calculation module is used to calculate the mean and standard deviation of the video distance set; The first calculation module is used to generate a video distance large flag for all video distances greater than the mean and greater than the standard deviation; The first calculation module is used to generate video distance and other identifiers for all video distances that are equal to the mean and the standard deviation; The first calculation module is used to generate a video distance difference identifier for all remaining video distances; The first calculation module is used to count the number of large identifiers of the video distance large identifier, count the number of equal identifiers of the video distance equal identifier, and count the number of difference identifiers of the video distance difference identifier; The first calculation module is used to calculate the sub-surveillance video analysis coefficient of each sub-surveillance video based on the number of large identifiers, the number of equal identifiers, and the number of difference identifiers.

9. The vehicle sampling abnormality warning system based on video monitoring according to claim 6 is characterized in that: The second calculation module is used for: The second calculation module is used to compare all sub-surveillance video analysis coefficients with corresponding sub-safety video analysis coefficients one by one, divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficients into a lower difference coefficient set, and sort the lower difference coefficient set by numerical value; The second calculation module is used to divide all sub-surveillance video coefficients equal to the sub-security video analysis coefficients into a set of equal difference coefficients; The second calculation module is used to divide all sub-surveillance video analysis coefficients that are smaller than the sub-safety video analysis coefficient into an upper difference coefficient set, and sort the lower difference coefficient set by numerical value; The second calculation module is used to calculate the vehicle abnormality warning analysis coefficient of the vehicle to be warned based on the lower difference coefficient set, the equal difference coefficient set and the upper difference coefficient set; ； ； ； Among them, w is the vehicle abnormality warning analysis coefficient of the vehicle to be abnormally warned, a is the adjustment coefficient of the vehicle abnormality warning analysis coefficient, e(g, k) is the comparison function between the upper difference coefficient set and the lower difference coefficient set, Δd is the weight corresponding to the sub-monitoring video analysis coefficient, and the value is [0.85, 1.25], f is the number of sub-monitoring video analysis coefficients in the upper difference coefficient set, β is the loss factor for calculating the vehicle abnormality warning analysis coefficient, and Δt i is the corresponding difference between the neutron surveillance video analysis coefficient of the upper difference coefficient set and the neutron surveillance video analysis coefficient of the lower difference coefficient set, max (Δt i ) is the difference Δt from all i The maximum value obtained in Δr 均 For all differences Δt i The average value of t i is the i-th sub-surveillance video analysis coefficient in the above difference coefficient set, r i is the sub-surveillance video analysis coefficient corresponding to the i-th sub-surveillance video analysis coefficient in the lower difference coefficient set; The second calculation module is used to determine the adjustment coefficient a of the vehicle abnormality warning analysis coefficient according to the following method: The second calculation module is used to count the equal number of sub-monitoring video analysis coefficients in the equal difference coefficient set; The second calculation module is used to calculate the sum Q of the sub-monitoring video analysis coefficients in the upper difference coefficient set and the lower difference coefficient set; The second calculation module is used to pre-set a first preset adjustment coefficient, a second preset adjustment coefficient and a third preset adjustment coefficient; The second calculation module is configured to use the first preset adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal number is less than 0.85Q; The second calculation module is configured to use the second preset adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal quantity is greater than or equal to 0.85Q and less than 1.25Q; The second calculation module is configured to use the third preset adjustment coefficient as the adjustment coefficient a of the vehicle abnormality warning analysis coefficient when the equal number is greater than 1.25Q.

10. The vehicle sampling abnormality warning system based on video monitoring according to claim 6 is characterized in that: The abnormal warning module is used to: The abnormality warning module is used to obtain a preset vehicle abnormality warning analysis coefficient, and determine whether the vehicle to be warned has an abnormality based on the relationship between the abnormality warning analysis coefficient and the preset abnormality warning analysis coefficient; The abnormality warning module is configured to determine that an abnormality exists in the vehicle to be warned when the abnormality warning analysis coefficient is less than the preset abnormality warning analysis coefficient; The abnormality warning module is used to determine that there is no abnormality in the vehicle to be warned when the abnormality warning analysis coefficient is greater than or equal to the preset abnormality warning analysis coefficient.

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