CN115457446A - Abnormal behavior supervision system based on video analysis - Google Patents
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Abstract
The invention discloses an abnormal behavior supervision system based on video analysis, which comprises a video acquisition module, a video analysis module, an alarm processing module and a configuration management module, wherein the video acquisition module is used for acquiring a video image; the video acquisition module is used for acquiring a monitoring video of a target person in a target scene; the video analysis module is used for inputting the monitoring video into the abnormal behavior detection neural network model frame by frame and judging whether the target person has abnormal behavior in the target scene according to the detection result; the alarm processing module is used for intercepting a monitoring video from an abnormal behavior starting frame to an abnormal behavior ending frame as an abnormal behavior video when judging that the target person has an abnormal behavior in a target scene; generating an abnormal behavior early warning instruction, and synchronously sending the abnormal behavior early warning instruction and the abnormal behavior video to a processing center; the configuration management module is used for maintaining and managing the supervisory program and adjusting the supervisory program. The invention can replace a manual monitoring mode, efficiently monitor the abnormal behavior of personnel in a target scene, and timely send out corresponding early warning prompt information.
Description
Technical Field
The invention belongs to the technical field of video monitoring and analysis, and particularly relates to an abnormal behavior supervision system based on video analysis.
Background
The target detection is a popular direction of computer vision and digital image processing, is widely applied to various fields of robot navigation, intelligent video monitoring, industrial detection, aerospace and the like, reduces the consumption of human capital through the computer vision, and has important practical significance. Therefore, target detection becomes a research hotspot of theory and application in recent years, and is an important branch of image processing and computer vision discipline and a core part of an intelligent monitoring system. Due to the wide application of deep learning, the target detection algorithm is developed rapidly.
One of the main purposes of video monitoring of the supervised area is to utilize related videos to perform real-time display and retrospective check on events occurring in the supervised area, and determine whether abnormal behaviors exist in personnel in the supervised area. At present, most of monitoring videos in a monitoring area are subjected to real-time abnormal monitoring in a manual watching mode, and abnormal retrospective mode is found through backtracking, so that the monitoring efficiency is extremely low, and the situation of insufficient monitoring is very easy to occur.
With the development of machine vision technology, the intelligent detection technology for the abnormal behaviors of the monitored video starts to appear correspondingly, so that prompt information is given in time when the abnormal behaviors are found in the monitored video. However, the currently commonly used abnormal behavior detection mainly refers to behavior detection of crossing and invading forbidden zones, has high detection accuracy, and cannot detect abnormal actions of people. And in some specific occasions, abnormal detection needs to be carried out on the action of the personnel, for example, when the person is moved, the action of the person is required to be detected so as to judge whether the actions of theft and financial damage exist. Under such a demand, the prior art has not yet satisfied the demand.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the defects in the prior art and provides an abnormal behavior supervision system based on video analysis.
The technical scheme is as follows: the abnormal behavior supervision system based on video analysis comprises a video acquisition module, a video analysis module, an alarm processing module and a configuration management module;
the video acquisition module is used for acquiring a monitoring video of a target person in a target scene;
the video analysis module is used for inputting the monitoring video into the abnormal behavior detection neural network model frame by frame, identifying and detecting the action of the target person in the target scene through the abnormal behavior detection neural network model, outputting a detection result and judging whether the target person has abnormal behavior in the target scene according to the detection result;
the alarm processing module is used for determining an abnormal behavior starting frame and an abnormal behavior ending frame according to a detection result when judging that the target person has an abnormal behavior in a target scene, and intercepting a monitoring video from the abnormal behavior starting frame to the abnormal behavior ending frame as an abnormal behavior video; generating an abnormal behavior early warning instruction, and synchronously sending the abnormal behavior early warning instruction and the abnormal behavior video to a processing center;
the configuration management module is used for maintaining and managing the supervisory program and adjusting the supervisory program.
Further, the acquiring, by the video acquiring module, the monitoring video of the target person in the target scene specifically includes: starting a camera module according to an instruction, and receiving a target person video in a target area collected by the camera module; and carrying out identification and contour analysis on a preset target on the video frame of the video.
Further, the abnormal behavior detection neural network model adopts a VGG deep neural network model, and a cross entropy loss function is adopted as a loss function.
Further, the method also comprises a training model module, wherein the training process of the training model module comprises the following steps:
acquiring a training sample of abnormal behavior and action of a person in a target scene;
carrying out image preprocessing on the training sample, and adjusting the size of the image;
inputting the preprocessed training samples into an abnormal behavior detection neural network model for training until the accuracy rate of the abnormal behavior detection neural network model on detecting abnormal behavior actions of the personnel in a target scene reaches a first set threshold value, and the recall rate reaches a second set threshold value.
Further, the inputting the preprocessed training samples into the abnormal behavior detection neural network model for training includes: and training an abnormal behavior detection neural network model by adopting a batch gradient descent algorithm and a back propagation algorithm.
Further, the process of identifying and detecting the action of the target person in the target scene by the abnormal behavior detection neural network model includes:
performing feature extraction on the frame image to obtain joint points of a target person and image coordinates corresponding to the joint points;
calculating the distance between each joint point according to the image coordinates corresponding to the joint points;
calculating the displacement difference between the same joint points of the adjacent frame images and the distance change value between the joint points;
and judging whether the action of the target person is abnormal behavior according to the displacement difference between the same joint points of the adjacent frame images and the distance change value between the joint points.
Further, the synchronous sending of the abnormal behavior early warning instruction and the abnormal behavior video to the processing center includes:
encrypting the abnormal behavior video by using a secret key to generate an encrypted video packet;
converting the early warning instruction into a binary code, and naming the encrypted video packet by the converted binary code;
and transmitting the named encrypted video packet to a processing center through an encryption channel.
The system further comprises a labeling module, wherein the labeling module detects abnormal behavior categories corresponding to each preset target and characteristic information thereof in the neural network model by using the abnormal behaviors, and labels and classifies each target person in the monitoring video of the obtained target person in the target scene according to the corresponding abnormal behavior category.
Furthermore, the configuration management module comprises site management, analysis model management, alarm plan management and server management.
Further, the site management is used for adding sites and configuring sites, camera execution plans, alarm plans and analysis models; the analysis model management is used for maintaining all alarm types and identifying whether the behavior analysis of personnel is illegal by using a camera; the alarm plan management is used for setting an alarm notification mode and notification time; the server management is used for maintaining server information and performing a rapid screening function.
Has the beneficial effects that: the method comprises the steps of inputting a monitoring video stream of a target person in a target scene into a corresponding abnormal behavior detection neural network model, then carrying out action recognition detection on the monitoring video through the abnormal behavior detection neural network model to intelligently and efficiently detect whether the target person has abnormal behaviors, intercepting the monitoring video from the beginning to the end of the abnormal behaviors as an abnormal behavior video when the target person has the abnormal behaviors, and generating an abnormal behavior early warning instruction to synchronously send the abnormal behavior video to a processing center so as to facilitate the central person to check and respond. By the method, a manual monitoring mode can be replaced, abnormal behaviors of people in a target scene can be efficiently monitored, and corresponding early warning prompt information can be timely sent out.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "inner", "outer", etc. indicate orientations or positional relationships that are shown merely for convenience in describing the present invention and to simplify the description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
An abnormal behavior supervision system based on video analysis comprises a video acquisition module, a video analysis module, an alarm processing module and a configuration management module;
the video acquisition module is used for acquiring a monitoring video of a target person in a target scene;
the video analysis module is used for inputting the monitoring video into the abnormal behavior detection neural network model frame by frame, identifying and detecting the action of the target person in the target scene through the abnormal behavior detection neural network model, outputting a detection result and judging whether the target person has abnormal behavior in the target scene according to the detection result;
the alarm processing module is used for determining an abnormal behavior starting frame and an abnormal behavior ending frame according to a detection result when judging that the target person has an abnormal behavior in a target scene, and intercepting a monitoring video from the abnormal behavior starting frame to the abnormal behavior ending frame as an abnormal behavior video; generating an abnormal behavior early warning instruction, and synchronously sending the abnormal behavior early warning instruction and an abnormal behavior video to a processing center;
the configuration management module is used for maintaining and managing the supervisory program and adjusting the supervisory program.
Example 2
An abnormal behavior supervision system based on video analysis comprises a video acquisition module, a video analysis module, an alarm processing module and a configuration management module;
the video acquisition module is used for acquiring a monitoring video of a target person in a target scene, and specifically comprises: starting a camera module according to an instruction, and receiving a target person video in a target area acquired by the camera module; performing identification and contour analysis of a preset target on a video frame of the video;
in the above embodiment provided by the embodiment of the present invention, preferably, the camera module has an infrared camera function, so as to record images under the condition of insufficient light at night; more preferably, the camera module still includes the light filling function, when utilizing camera module to record a video to the target area, can provide extra light, the quality of the video of being convenient for.
The video analysis module is used for inputting the monitoring video into the abnormal behavior detection neural network model frame by frame, identifying and detecting the action of the target person in the target scene through the abnormal behavior detection neural network model, outputting a detection result and judging whether the target person has abnormal behavior in the target scene according to the detection result;
in this embodiment, the abnormal behavior detection neural network model adopts a VGG deep neural network model, and the loss function thereof adopts a cross entropy loss function.
The VGG deep neural network model can be selected from a VGGNet-16 network structure model, and can input RGB pictures of 224 × 224 pixels, and eight network layers are arranged: the first layer was convolved 2 times with a 3 x 3 convolution kernel, outputting 64 feature maps each time, and performing maximum pooling max boosting; the second layer was convolved 2 times with a 3 x 3 convolution kernel, outputting 128 feature maps each time, and performing maximum pooling max boosting; the third layer uses a convolution kernel of 3 × 3, and is convoluted 3 times, 256 feature maps are output each time, and the maximum pooling max power is carried out; the fourth layer uses a convolution kernel of 3 x 3 to convolute for 3 times, 512 feature maps are output each time, and maximum pooling max power is carried out; the fifth layer uses a convolution kernel of 3 × 3, and the convolution is carried out 3 times, 512 feature maps are output each time, and maximum pooling max posing is carried out; the sixth layer, the seventh layer and the eighth layer use full connection layers, and respectively comprise 4096 hidden layers, 4096 hidden layers and 1000 hidden layers. That is, only 1000 eigenvalues remain to the fully connected layer; and finally, activating a function through softmax to obtain a final result.
A training model module, the training model module training process comprising:
acquiring a training sample of abnormal behavior and action of a person in a target scene;
carrying out image preprocessing on the training sample, and adjusting the size of the image;
inputting the preprocessed training samples into an abnormal behavior detection neural network model for training until the accuracy of the abnormal behavior detection neural network model in detecting abnormal behavior actions of the personnel in the target scene reaches a first set threshold, and the recall rate reaches a second set threshold.
The training process can adopt a batch gradient descent algorithm and a back propagation algorithm to train the abnormal behavior detection neural network model, and the batch gradient descent algorithm and the back propagation algorithm adopt an integral training algorithm, which specifically comprises the following steps: (1) randomly inputting a certain number of training sample images; (2) Forward propagating the network and calculating a loss function and an error response; (3) a back propagation network; (4) updating all parameters; (5) Repeating steps (1) - (4) until the result of the loss function no longer falls.
The alarm processing module is used for determining an abnormal behavior starting frame and an abnormal behavior ending frame according to a detection result when judging that the target person has an abnormal behavior in a target scene, and intercepting a monitoring video from the abnormal behavior starting frame to the abnormal behavior ending frame as an abnormal behavior video; generating an abnormal behavior early warning instruction, and synchronously sending the abnormal behavior early warning instruction and the abnormal behavior video to a processing center;
the configuration management module is used for maintaining and managing the supervisory programs and adjusting the supervisory programs.
Example 3
An abnormal behavior supervision system based on video analysis comprises a video acquisition module, a video analysis module, an alarm processing module and a configuration management module;
the video acquisition module is used for acquiring a monitoring video of a target person in a target scene, and specifically comprises: starting a camera module according to an instruction, and receiving a target person video in a target area acquired by the camera module; carrying out recognition and contour analysis of a preset target on a video frame of the video;
in the above embodiment provided by the embodiment of the present invention, preferably, the camera module has an infrared camera function, so as to record images under the condition of insufficient light at night; more preferably, the camera module further comprises a light supplementing function, and when the camera module is used for recording a video of a target area, extra light can be provided, so that the quality of the video recording is facilitated.
The video analysis module is used for inputting the monitoring video into the abnormal behavior detection neural network model frame by frame, identifying and detecting the action of the target person in the target scene through the abnormal behavior detection neural network model, outputting a detection result and judging whether the target person has abnormal behavior in the target scene according to the detection result;
in this embodiment, the abnormal behavior detection neural network model adopts a VGG deep neural network model, and the loss function thereof adopts a cross entropy loss function.
The VGG deep neural network model can be selected from a VGGNet-16 network structure model, and can input RGB pictures of 224 × 224 pixels, and eight network layers are arranged: the first layer was convolved 2 times with a 3 x 3 convolution kernel, outputting 64 feature maps each time, and performing maximum pooling max boosting; the second layer was convolved 2 times with a convolution kernel of 3 x 3, outputting 128 feature maps each time, and performing maximal pooling max boosting; the third layer uses a convolution kernel of 3 × 3, and is convoluted 3 times, 256 feature maps are output each time, and the maximum pooling max power is carried out; the fourth layer uses a convolution kernel of 3 x 3 to convolute for 3 times, 512 feature maps are output each time, and maximum pooling max power is carried out; the fifth layer uses a convolution kernel of 3 × 3, and the convolution is carried out 3 times, 512 feature maps are output each time, and maximum pooling max posing is carried out; the sixth layer, the seventh layer and the eighth layer use full connection layers, and respectively comprise 4096 hidden layers, 4096 hidden layers and 1000 hidden layers. That is, only 1000 eigenvalues remain to the fully connected layer; and finally, activating a function through softmax to obtain a final result.
A training model module, the training model module training process comprising:
acquiring a training sample of abnormal behavior and action of a person in a target scene;
carrying out image preprocessing on the training sample, and adjusting the size of the image;
inputting the preprocessed training samples into an abnormal behavior detection neural network model for training until the accuracy of the abnormal behavior detection neural network model in detecting abnormal behavior actions of the personnel in the target scene reaches a first set threshold, and the recall rate reaches a second set threshold.
The training process can adopt a batch gradient descent algorithm and a back propagation algorithm to train the abnormal behavior detection neural network model, and the batch gradient descent algorithm and the back propagation algorithm adopt an integral training algorithm, which specifically comprises the following steps: (1) randomly inputting a certain number of training sample images; (2) Forward propagating the network and calculating a loss function and an error response; (3) a back propagation network; (4) updating all parameters; (5) Repeating steps (1) - (4) until the result of the loss function no longer drops.
In this embodiment, the process of identifying and detecting the action of the target person in the target scene by the abnormal behavior detection neural network model includes:
performing feature extraction on the frame image to obtain joint points of a target person and image coordinates corresponding to the joint points;
calculating the distance between each joint point according to the image coordinates corresponding to the joint points;
calculating the displacement difference between the same joint points of the adjacent frame images and the distance change value between the joint points;
and judging whether the action of the target person is abnormal behavior according to the displacement difference between the same joint points of the adjacent frame images and the distance change value between the joint points.
The alarm processing module is used for determining an abnormal behavior starting frame and an abnormal behavior ending frame according to a detection result when judging that the target person has an abnormal behavior in a target scene, and intercepting a monitoring video from the abnormal behavior starting frame to the abnormal behavior ending frame as an abnormal behavior video; generating an abnormal behavior early warning instruction, and synchronously sending the abnormal behavior early warning instruction and an abnormal behavior video to a processing center;
when the method is specifically implemented, the process of synchronously sending the abnormal behavior early warning instruction and the abnormal behavior video to the processing center comprises the following steps: encrypting the abnormal behavior video by using a secret key to generate an encrypted video packet; converting the early warning instruction into a binary code, and naming the encrypted video packet by the converted binary code; and transmitting the named encrypted video packet to a processing center through an encryption channel. The key encryption algorithm can adopt a symmetric encryption algorithm, such as DES, 3DES, AES, blowfish and the like; asymmetric encryption algorithms such as RSA, DSA, DSS, ELGamal, etc.; and a one-way encryption algorithm, such as MD5, sha1, sha224 and the like.
And the labeling module is used for detecting abnormal behavior categories corresponding to each preset target and characteristic information thereof in the neural network model by using the abnormal behaviors and labeling and classifying each target person in the monitoring video of the obtained target person in the target scene according to the corresponding abnormal behavior category.
The configuration management module is used for maintaining and managing the supervisory program and adjusting the supervisory program.
In this embodiment, the configuration management module includes location management, analysis model management, alarm plan management, and server management.
In this embodiment, the site management is used to add sites and configure sites, camera execution plans, alarm plans, and analysis models; the analysis model management is used for maintaining all alarm types and identifying whether the behavior analysis of personnel is illegal by a camera; the alarm plan management is used for setting an alarm notification mode and notification time; the server management is used for maintaining server information and performing a rapid screening function.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An abnormal behavior supervision system based on video analysis is characterized in that: the system comprises a video acquisition module, a video analysis module, an alarm processing module and a configuration management module;
the video acquisition module is used for acquiring a monitoring video of a target person in a target scene;
the video analysis module is used for inputting the monitoring video into the abnormal behavior detection neural network model frame by frame, identifying and detecting the action of the target person in the target scene through the abnormal behavior detection neural network model, outputting a detection result and judging whether the target person has abnormal behavior in the target scene according to the detection result;
the alarm processing module is used for determining an abnormal behavior starting frame and an abnormal behavior ending frame according to a detection result when judging that the target person has an abnormal behavior in a target scene, and intercepting a monitoring video from the abnormal behavior starting frame to the abnormal behavior ending frame as an abnormal behavior video; generating an abnormal behavior early warning instruction, and synchronously sending the abnormal behavior early warning instruction and the abnormal behavior video to a processing center;
the configuration management module is used for maintaining and managing the supervisory program and adjusting the supervisory program.
2. The system according to claim 1, wherein the system comprises: the video acquiring module is used for acquiring the monitoring video of the target person in the target scene, and specifically comprises the following steps: starting a camera module according to an instruction, and receiving a target person video in a target area acquired by the camera module; and carrying out recognition and contour analysis on a preset target on the video frame of the video.
3. The system according to claim 1, wherein the system comprises: the abnormal behavior detection neural network model adopts a VGG deep neural network model, and the loss function adopts a cross entropy loss function.
4. The system according to claim 1, wherein the system comprises: the training model module is used for training the training process, and the training process of the training model module comprises the following steps:
acquiring a training sample of abnormal behavior and action of a person in a target scene;
carrying out image preprocessing on the training sample, and adjusting the size of the image;
inputting the preprocessed training samples into an abnormal behavior detection neural network model for training until the accuracy of the abnormal behavior detection neural network model in detecting abnormal behavior actions of the personnel in the target scene reaches a first set threshold, and the recall rate reaches a second set threshold.
5. The system according to claim 4, wherein the system comprises: inputting the preprocessed training samples into an abnormal behavior detection neural network model for training, wherein the training comprises the following steps: and training an abnormal behavior detection neural network model by adopting a batch gradient descent algorithm and a back propagation algorithm.
6. The system according to claim 1, wherein the system comprises: the process of identifying and detecting the action of the target person in the target scene by the abnormal behavior detection neural network model comprises the following steps:
performing feature extraction on the frame image to obtain joint points of a target person and image coordinates corresponding to the joint points;
calculating the distance between each joint point according to the image coordinates corresponding to the joint points;
calculating the displacement difference between the same joint points of the adjacent frame images and the distance change value between the joint points;
and judging whether the action of the target person is abnormal behavior according to the displacement difference between the same joint points of the adjacent frame images and the distance change value between the joint points.
7. The system according to claim 1, wherein the system comprises: the abnormal behavior early warning instruction and the abnormal behavior video are synchronously sent to a processing center, and the method comprises the following steps:
encrypting the abnormal behavior video by using a secret key to generate an encrypted video packet;
converting the early warning instruction into a binary code, and naming the encrypted video packet by the converted binary code;
and transmitting the named encrypted video packet to a processing center through an encryption channel.
8. The system according to claim 1, wherein the system comprises: the system further comprises a labeling module, wherein the labeling module is used for detecting abnormal behavior categories corresponding to all preset targets and characteristic information thereof in the neural network model by using the abnormal behaviors, and labeling and classifying all target personnel in the monitoring video of the obtained target personnel in the target scene according to the corresponding abnormal behavior categories.
9. The system according to claim 1, wherein the system comprises: the configuration management module comprises site management, analysis model management, alarm plan management and server management.
10. The system according to claim 9, wherein the system comprises: the place management is used for adding places and configuring the places, the camera execution plan, the alarm plan and the analysis model; the analysis model management is used for maintaining all alarm types and identifying whether the behavior analysis of personnel is illegal by using a camera; the alarm plan management is used for setting an alarm notification mode and notification time; the server management is used for maintaining server information and performing a rapid screening function.
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| CN116385948A (en) * | 2023-06-07 | 2023-07-04 | 成都考拉悠然科技有限公司 | System and method for early warning railway side slope abnormality |
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| CN117542000A (en) * | 2024-01-10 | 2024-02-09 | 百工汇智(苏州)智能科技有限公司 | Warehouse safety monitoring method and system combining visual detection |
| CN117749995A (en) * | 2023-12-22 | 2024-03-22 | 深圳市智安天下科技有限公司 | Video monitoring method and system based on multi-scene recognition and voice interaction |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116385948A (en) * | 2023-06-07 | 2023-07-04 | 成都考拉悠然科技有限公司 | System and method for early warning railway side slope abnormality |
| CN116385948B (en) * | 2023-06-07 | 2023-09-15 | 成都考拉悠然科技有限公司 | System and method for early warning railway side slope abnormality |
| CN117528028A (en) * | 2023-11-08 | 2024-02-06 | 广州雄风信息技术有限公司 | Security video monitoring system based on Internet of things |
| CN117528028B (en) * | 2023-11-08 | 2024-04-19 | 广州雄风信息技术有限公司 | Security video monitoring system based on Internet of things |
| CN117478843A (en) * | 2023-11-15 | 2024-01-30 | 廊坊博联科技发展有限公司 | Intelligent patrol control system and method |
| CN117749995A (en) * | 2023-12-22 | 2024-03-22 | 深圳市智安天下科技有限公司 | Video monitoring method and system based on multi-scene recognition and voice interaction |
| CN117542000A (en) * | 2024-01-10 | 2024-02-09 | 百工汇智(苏州)智能科技有限公司 | Warehouse safety monitoring method and system combining visual detection |
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