CN117437696A - Behavior monitoring analysis method, system, equipment and medium based on deep learning - Google Patents
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Abstract
The application discloses a behavior monitoring analysis method, system, equipment and medium based on deep learning, which mainly relate to the technical field of behavior monitoring analysis and are used for solving the problem of low accuracy of analysis results of the existing scheme. Comprising the following steps: acquiring a behavior video of an examinee in real time through mobile acquisition equipment so as to obtain a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; the processed image is used as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further the position matting of the examinee is obtained; taking the position matting of the examinee as the input of a human skeleton key point detection algorithm to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the final behavior category is obtained by utilizing a position detection convolutional neural network; and when the behavior category does not meet the requirements, carrying out voice reminding.
Description
Technical Field
The application relates to the technical field of behavior monitoring analysis, in particular to a behavior monitoring analysis method, system, equipment and medium based on deep learning.
Background
With the development of network technology, online examination is becoming a mainstream examination form. However, the difficulty of proctoring an on-line test is high, and there may be various actions that violate the rule of the test, such as taking up and leaving the test interface to look up a book, etc., which may seriously affect fairness of the test. Therefore, how to effectively monitor the behaviors of online test takers and timely discover and correct illegal behaviors is a current urgent problem to be solved.
In order to solve the problem, a large number of invigorator staff are dispatched to monitor the monitoring pictures of a plurality of examinees in real time. Or the face recognition algorithm is used for monitoring a plurality of examinees in real time, specifically, face image capturing is carried out in real time in the electric power line examination process, and examination interface locking processing is carried out when no face is captured or no examination face is captured, so that secret management of examination contents in the examination process is realized, the guarantee force of examination safety in the prevention of the tilapia is effectively improved, and examination behaviors and answer state analysis are carried out in the on-line examination cheating behaviors.
However, in the monitoring method for the prison staff, the analysis of the video is subjective due to the physiological characteristics of the prison staff, and the video picture is intensively examined for a long time and in high tension, so that visual fatigue can be quickly generated, and the cheating behaviors of the examinee occasionally appearing in the monitoring video are difficult to judge timely and accurately. The monitoring method for capturing the face image is mainly focused on face analysis, and the accuracy of an analysis result is low.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a behavior monitoring analysis method, a behavior monitoring analysis system, behavior monitoring analysis equipment and a behavior monitoring analysis medium based on deep learning, so as to solve the problem of low accuracy of analysis results of the existing scheme.
In a first aspect, the present application provides a behavior monitoring analysis method based on deep learning, where the method includes: acquiring a behavior video of an examinee in real time through mobile acquisition equipment so as to obtain a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; the processed image is used as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further the position matting of the examinee is obtained; the prediction output layer of the position detection convolutional neural network adopts a decoupling head mode; taking the position matting of the examinee as the input of a human skeleton key point detection algorithm to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
Further, after the moving acquisition device acquires the behavior video of the examinee in real time so as to obtain the video frame image, the method further comprises: and determining whether the video frame images meet preset standards, and if not, popping up a prompt box of the correction mobile acquisition device.
Further, the image enhancement of the video frame image specifically includes: by the formula:image enhancement is carried out on the video frame image; wherein (1)>To output an imagePlain value of->For the original image pixel values, A and +.>Is a preset enhancement constant.
Further, the method further comprises: after triggering the voice prompt, storing a video frame image triggering the voice prompt.
In a second aspect, the present application provides a behavior monitoring analysis system based on deep learning, the system comprising: the acquisition module is used for acquiring the behavior video of the examinee in real time through the mobile acquisition equipment so as to acquire a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; the processed image is used as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further the position matting of the examinee is obtained; the prediction output layer of the position detection convolutional neural network adopts a decoupling head mode; the reminding module is used for taking the candidate position matting as the input of a human skeleton key point detection algorithm so as to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
Further, the system also comprises a prompt module for determining whether the video frame image meets the preset standard, and if not, popping up a prompt box of the correction mobile acquisition device.
Further, the obtaining module includes a prompting unit configured to pass through the formula:image enhancement is carried out on the video frame image; wherein (1)>For outputting pixel values +.>For the original image pixel values, A and +.>Is a preset enhancement constant.
Further, the system also comprises a storage module for storing the video frame image triggering the voice prompt after triggering the voice prompt.
In a third aspect, the present application provides a behavior monitoring analysis device based on deep learning, the device comprising: a processor; and a memory having executable code stored thereon that, when executed, causes the processor to perform a deep learning based behavior monitoring analysis method as in any of the above.
In a fourth aspect, the present application provides a non-volatile computer storage medium having stored thereon computer instructions that, when executed, implement a deep learning based behavior monitoring analysis method as in any of the above.
As can be appreciated by those skilled in the art, the present application has at least the following beneficial effects:
the mobile acquisition equipment is responsible for acquiring video frame images of on-line examinees in real time; preprocessing the captured video frame image of the examinee, such as image enhancement, image size adjustment and the like; extracting the position information of the examinee through a position detection convolutional neural network; judging whether the limb of the examinee is completely positioned in the frame or not through a human skeleton key point detection algorithm, and checking the behavior category (whether standing behaviors exist) by utilizing a Softmax activation function; when the test taker has standing behaviors or the limbs leave the monitoring picture, the abnormal behavior reminding module sends out reminding to remind the test taker to standardize the answering behaviors of the test; in addition, the method and the device can save the video frame image with suspected cheating behaviors, and facilitate later-period manual examination and verification.
Drawings
Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which:
fig. 1 is a flowchart of a behavior monitoring analysis method based on deep learning according to an embodiment of the present application.
Fig. 2 is a schematic diagram of an internal structure of a behavior monitoring analysis system based on deep learning according to an embodiment of the present application.
Fig. 3 is a schematic diagram of an internal structure of a behavior monitoring and analyzing device based on deep learning according to an embodiment of the present application.
Detailed Description
It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not represent that the present disclosure can be realized only by the preferred embodiments, which are merely for explaining the technical principles of the present disclosure, not for limiting the scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments that may be obtained by one of ordinary skill in the art without inventive effort shall still fall within the scope of the present disclosure.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The following describes in detail the technical solution proposed in the embodiments of the present application through the accompanying drawings.
The embodiment of the application provides a behavior monitoring analysis method based on deep learning, as shown in fig. 1, and the method mainly comprises the following steps:
step 110, acquiring a behavior video of an examinee in real time through mobile acquisition equipment so as to obtain a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; and taking the processed image as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further obtaining the position matting of the examinee.
The prediction output layer of the position detection convolutional neural network adopts a decoupling head mode and is divided into three branches, and the foreground and background, the position frame coordinates and the categories are respectively predicted.
In addition, after the video of the behavior of the examinee is acquired in real time through the mobile acquisition equipment, and then the video frame image is obtained, the video frame image can be checked, whether the video frame image meets the preset standard or not is determined, and when the video frame image does not meet the preset standard (when the image quality is poor), a prompt box of the mobile acquisition equipment is popped up to correct, so that the examinee is prompted to correct position light and the like.
The image enhancement of the video frame image can be specifically:
by the formula:image enhancement is carried out on the video frame image; wherein (1)>In order to output the pixel value(s),for the original image pixel values, A and +.>Is a preset enhancement constant.
Step 120, taking the candidate position matting as the input of a human skeleton key point detection algorithm to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
The method can also be used for data storage, and is convenient for later-stage manual examination and verification.
The method comprises the following steps: after triggering the voice prompt, storing a video frame image triggering the voice prompt.
In addition, fig. 2 is a schematic diagram of a behavior monitoring analysis system based on deep learning according to an embodiment of the present application. As shown in fig. 2, the system provided in the embodiment of the present application mainly includes:
the obtaining module 210 is configured to collect the behavior video of the examinee in real time through the mobile collection device, so as to obtain a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; and taking the processed image as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further obtaining the position matting of the examinee.
The obtaining module 210 includes a prompting unit configured to perform a method of:image enhancement is carried out on the video frame image; wherein (1)>For outputting pixel values +.>For the original image pixel values, A and +.>Is a preset enhancement constant.
The prediction output layer of the position detection convolutional neural network adopts a decoupling head mode.
The reminding module 220 is used for taking the candidate position matting as the input of a human skeleton key point detection algorithm so as to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
The system also comprises a prompt module which is used for determining whether the video frame images accord with the preset standard, and when the video frame images do not accord with the preset standard, a prompt box of the correction mobile acquisition device is popped up.
The system also comprises a storage module for storing the video frame image triggering the voice prompt after triggering the voice prompt.
The above is a method embodiment in the present application, and based on the same inventive concept, the embodiment of the present application further provides a behavior monitoring analysis device based on deep learning. As shown in fig. 3, the apparatus includes: a processor; and a memory having executable code stored thereon that, when executed, causes the processor to perform a deep learning based behavior monitoring analysis method as in the above embodiments.
Specifically, the server acquires the behavior video of the examinee in real time through the mobile acquisition equipment, so as to obtain a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; the processed image is used as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further the position matting of the examinee is obtained; the prediction output layer of the position detection convolutional neural network adopts a decoupling head mode; taking the position matting of the examinee as the input of a human skeleton key point detection algorithm to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
In addition, the embodiment of the application also provides a nonvolatile computer storage medium, on which executable instructions are stored, and when the executable instructions are executed, the behavior monitoring analysis method based on deep learning is realized.
Thus far, the technical solution of the present disclosure has been described in connection with the foregoing embodiments, but it is easily understood by those skilled in the art that the protective scope of the present disclosure is not limited to only these specific embodiments. The technical solutions in the above embodiments may be split and combined by those skilled in the art without departing from the technical principles of the present disclosure, and equivalent modifications or substitutions may be made to related technical features, which all fall within the scope of the present disclosure.
Claims (10)
1. A behavior monitoring analysis method based on deep learning, the method comprising:
acquiring a behavior video of an examinee in real time through mobile acquisition equipment so as to obtain a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; the processed image is used as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further the position matting of the examinee is obtained; the prediction output layer of the position detection convolutional neural network adopts a decoupling head mode;
taking the position matting of the examinee as the input of a human skeleton key point detection algorithm to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
2. The behavior monitoring analysis method based on deep learning according to claim 1, wherein after collecting the behavior video of the examinee in real time by the mobile collection device, thereby obtaining the video frame image, the method further comprises:
and determining whether the video frame images meet preset standards, and if not, popping up a prompt box of the correction mobile acquisition device.
3. The behavior monitoring analysis method based on deep learning according to claim 1, wherein the image enhancement is performed on the video frame image, and specifically comprises:
by the formula:image enhancement is carried out on the video frame image;
wherein,for outputting pixel values +.>For the original image pixel values, A and +.>Is a preset enhancement constant.
4. The deep learning based behavioral monitoring and analysis method of claim 1 further comprising:
after triggering the voice prompt, storing a video frame image triggering the voice prompt.
5. A behavior monitoring analysis system based on deep learning, the system comprising:
the acquisition module is used for acquiring the behavior video of the examinee in real time through the mobile acquisition equipment so as to acquire a video frame image; performing image enhancement on the video frame image, and adjusting the image after image enhancement into a preset format to obtain a processed image; the processed image is used as the input of a trained position detection convolutional neural network to obtain the position coordinates of the examinee, and further the position matting of the examinee is obtained; the prediction output layer of the position detection convolutional neural network adopts a decoupling head mode;
the reminding module is used for taking the candidate position matting as the input of a human skeleton key point detection algorithm so as to obtain skeleton key point positions; determining whether the positions of the skeletal key points meet preset requirements or not, and performing voice reminding when the positions of the skeletal key points do not meet the preset requirements; the position detection convolutional neural network is utilized, output data of branches after decoupling heads of a prediction output layer are taken, and a Softmax activation function is input to obtain a final behavior class; and when the behavior category does not meet the requirements, carrying out voice reminding.
6. The deep learning based behavioral monitoring and analysis system of claim 5 further comprising a prompt module,
and the prompt box is used for determining whether the video frame images accord with preset standards, and when the video frame images do not accord with the preset standards, the prompt box of the correction mobile acquisition equipment is popped up.
7. The deep learning based behavioral monitoring and analysis system of claim 5 where the acquisition module includes a prompt unit,
for passing through the formula:image enhancement is carried out on the video frame image;
wherein,for outputting pixel values +.>For the original image pixel values, A and +.>Is a preset enhancement constant.
8. The deep learning based behavioral monitoring and analysis system of claim 5 further comprising a storage module,
and the video frame image for triggering the voice prompt is stored after triggering the voice prompt.
9. A behavior monitoring analysis device based on deep learning, the device comprising:
a processor;
and a memory having executable code stored thereon that, when executed, causes the processor to perform a deep learning based behavior monitoring analysis method as recited in any one of claims 1-4.
10. A non-transitory computer storage medium having stored thereon computer instructions that, when executed, implement a deep learning based behavior monitoring analysis method according to any of claims 1-4.
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