CN112200036A - Student behavior remote monitoring method and system - Google Patents

Student behavior remote monitoring method and system Download PDF

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CN112200036A
CN112200036A CN202011048828.6A CN202011048828A CN112200036A CN 112200036 A CN112200036 A CN 112200036A CN 202011048828 A CN202011048828 A CN 202011048828A CN 112200036 A CN112200036 A CN 112200036A
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樊星
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Shanghai Squirrel Classroom Artificial Intelligence Technology Co Ltd
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Abstract

The invention provides a remote monitoring method and a remote monitoring system for student behaviors, which can carry out video shooting on a student and extract a plurality of corresponding images, determine a corresponding target image pair according to pixel correlation information of two adjacent preprocessed images, finally judge whether the student behaviors are normal or not according to image similarity between the two images contained in the target image pair, and carry out corresponding warning operation.

Description

Student behavior remote monitoring method and system
Technical Field
The invention relates to the technical field of intelligent education, in particular to a student behavior remote monitoring method and system.
Background
Currently, behavior monitoring of a student during a class is generally realized by tracking and shooting the student, and specifically, a class video about the student is shot, and manual frame-by-frame browsing and screening are performed on the class video to judge whether the student has abnormal behavior during the class. But instead. The behavior monitoring mode not only needs to consume a large amount of manpower and material resources to browse and screen massive video data, but also easily causes browsing omission in a manual mode, and in addition, the behavior monitoring mode cannot effectively and accurately screen out slight abnormal behaviors made by students, so that the automation, the reliability and the accuracy of behavior monitoring of the students are seriously influenced. It is therefore desirable in the art to be able to remotely monitor the behavior of a student during a lesson in a comprehensive, automatic and accurate manner so as to effectively discriminate various types of abnormal behaviors made by the student.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a remote monitoring method and a remote monitoring system for student behaviors, which are characterized in that a student is shot to obtain a video of the student in a preset time period, the video is subjected to image extraction processing to obtain a plurality of images, the images are preprocessed to obtain pixel correlation information between any two adjacent images, the two adjacent images are determined as a target image pair according to the pixel correlation information, image similarity information between the two images contained in the target image pair is obtained, whether the student behavior is normal or not is judged according to the image similarity information, and corresponding warning operation is carried out according to the judged result; therefore, the student behavior remote monitoring method and the student behavior remote monitoring system can carry out video shooting on a student and extract a plurality of corresponding images, determine a corresponding target image pair according to the pixel correlation information of two adjacent preprocessed images, finally judge whether the behavior of the student is normal or not according to the image similarity between the two images contained in the target image pair, and carry out corresponding warning operation.
The invention provides a remote monitoring method for student behaviors, which is characterized by comprising the following steps:
step S1, shooting a student to obtain a video of the student in a preset time period, and performing image extraction processing on the video to obtain a plurality of images;
step S2, after preprocessing a plurality of images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information;
step S3, acquiring image similarity information between two images included in the target image pair, judging whether the behavior of the student is normal according to the image similarity information, and performing corresponding warning operation according to the judgment result;
further, in step S1, capturing a student to obtain a video of the student within a preset time period, and performing image extraction processing on the video to obtain a plurality of images specifically includes:
step S101, carrying out panoramic shooting on the students so as to obtain videos of the students in the preset time period;
step S102, according to a preset time interval and a forward playing time sequence along the video, carrying out image extraction processing on the video so as to obtain a plurality of images;
further, in step S2, after preprocessing the images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information specifically includes:
step S201, performing Kalman filtering noise reduction processing and image pixel smoothing processing on a plurality of images in sequence;
step S202, dividing each image into N rectangular image sub-regions with the same area, and determining a pixel linear correlation coefficient between any two adjacent images according to the following formula (1):
Figure BDA0002708883220000031
in the above formula (1), R (a, b) represents a pixel linear correlation coefficient between the adjacent images a and b, SiRepresenting pixel chrominance values, G, of the ith rectangular image sub-area of image biPixel chrominance values, F, of the ith rectangular image sub-region representing image aiThe texture value of the pixel, K, representing the ith rectangular image sub-region of image biThe texture value of the pixel of the ith rectangular image sub-region of the image a is represented, theta represents a preset chroma weight value and takes a value of 0.4, delta represents a preset texture weight value and takes a value of 0.6, and X represents a common correction coefficient of the image a and the image b and takes a value of [0.7, 0.9]；
Step S203, comparing a pixel linear correlation coefficient R (a, b) between the adjacent images a and b with a preset pixel linear correlation threshold, if the pixel linear correlation coefficient R (a, b) is greater than or equal to the preset pixel linear correlation threshold, taking the current adjacent images a and b as the target image pair, otherwise, repeating the step S202 to calculate a pixel linear correlation coefficient between the next group of adjacent two images until the calculated pixel linear correlation coefficient is greater than or equal to the preset pixel linear correlation threshold;
further, in step S3, the acquiring image similarity information between two images included in the target image pair, determining whether the behavior of the student is normal according to the image similarity information, and performing corresponding warning operation according to the determination result specifically includes:
step S301, determining an image similarity value between two images included in the target image pair according to the following formula (2):
Figure BDA0002708883220000032
in the above formula (2), sim represents an image similarity value between two images included in the target image pair, MdRepresenting the number Q of rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to one image in the target image paircRepresenting moments of pixels related to the student's body in N rectangular image sub-regions corresponding to the other image in the target image pairNumber of shape image sub-regions, UjA pixel texture value, T, of a jth rectangular image sub-region of the rectangular image sub-region representing that one of the target image pair contains the student body related pixelvThe texture value of the pixel of the v-th rectangular image sub-area of the rectangular image sub-area which represents that the other image in the target image pair contains the pixel related to the student body is epsilon, the epsilon represents a first texture compensation coefficient, and the value of epsilon is [0.1, 0.15 ]]And beta represents a second texture compensation coefficient and takes a value of [0.05, 0.15 ]]；
Step S302, comparing the image similarity value sim with a preset image similarity threshold, if the image similarity value sim is greater than or equal to the preset image similarity threshold, judging that the behavior of the student is abnormal, otherwise, judging that the behavior of the student is normal;
step S303, when the behavior of the student is judged to be abnormal, voice warning information is sent to the student.
The invention also provides a student behavior remote monitoring system which is characterized by comprising a video shooting module, an image extraction module, a target image pair determination module, a student behavior state judgment module and a warning operation module; wherein the content of the first and second substances,
the video shooting module is used for shooting a student so as to obtain a video of the student in a preset time period;
the image extraction module is used for carrying out image extraction processing on the video so as to obtain a plurality of images;
the target image pair determining module is used for preprocessing a plurality of images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information;
the student behavior state judging module is used for acquiring image similarity information between two images contained in the target image pair and judging whether the behavior of the student is normal or not according to the image similarity information;
the warning operation module is used for carrying out corresponding warning operation according to the judgment result;
further, the video shooting module shoots students, so as to obtain videos of the students in a preset time period specifically comprises:
carrying out panoramic shooting on the students so as to obtain videos of the students in the preset time period;
and the number of the first and second groups,
the image extraction module performs image extraction processing on the video, so as to obtain a plurality of images specifically comprises:
according to a preset time interval and a forward playing time sequence along the video, carrying out image extraction processing on the video to obtain a plurality of images;
further, the step of, after the target image pair determining module preprocesses the plurality of images, acquiring pixel correlation information between any two adjacent images, and determining, according to the pixel correlation information, two adjacent images as a target image pair specifically includes:
sequentially carrying out Kalman filtering noise reduction processing and image pixel smoothing processing on a plurality of images;
dividing each image into N rectangular image sub-areas with the same area, and determining a pixel linear correlation coefficient between any two adjacent images according to the following formula (1):
Figure BDA0002708883220000051
in the above formula (1), R (a, b) represents a pixel linear correlation coefficient between the adjacent images a and b, SiRepresenting pixel chrominance values, G, of the ith rectangular image sub-area of image biPixel chrominance values, F, of the ith rectangular image sub-region representing image aiThe texture value of the pixel, K, representing the ith rectangular image sub-region of image biDenotes the pixel texture value of the ith rectangular image sub-region of the image a, and theta denotes the preset chroma weightA value of 0.4, delta a preset texture weight value of 0.6, and X a common correction coefficient of the image a and the image b of [0.7, 0.9 ]]；
Comparing the pixel linear correlation coefficient R (a, b) between the adjacent images a and b with a preset pixel linear correlation threshold, if the pixel linear correlation coefficient R (a, b) is greater than or equal to the preset pixel linear correlation threshold, taking the current adjacent images a and b as the target image pair, otherwise, repeating the step S202 to calculate the pixel linear correlation coefficient between the next group of adjacent two images until the calculated pixel linear correlation coefficient is greater than or equal to the preset pixel linear correlation threshold;
further, the student behavior state judgment module acquires image similarity information between two images included in the target image pair, and judges whether the behavior of the student is normal or not specifically according to the image similarity information:
determining an image similarity value between two images comprised by the target image pair according to the following formula (2):
Figure BDA0002708883220000061
in the above formula (2), sim represents an image similarity value between two images included in the target image pair, MdRepresenting the number Q of rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to one image in the target image paircRepresenting the number, U, of the rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to the other image in the target image pairjA pixel texture value, T, of a jth rectangular image sub-region of the rectangular image sub-region representing that one of the target image pair contains the student body related pixelvA rectangular image representing that the other image of the pair of target images contains pixels related to the student's bodyThe pixel texture value of the v-th rectangular image sub-region of the sub-region, epsilon represents the first texture compensation coefficient and the value thereof is [0.1, 0.15%]And beta represents a second texture compensation coefficient and takes a value of [0.05, 0.15 ]]；
Comparing the image similarity value sim with a preset image similarity threshold, if the image similarity value sim is greater than or equal to the preset image similarity threshold, judging that the behavior of the student is abnormal, otherwise, judging that the behavior of the student is normal;
and the number of the first and second groups,
the warning operation module carries out corresponding warning operation according to the judgment result, and specifically comprises the following steps:
and when the behavior of the student is judged to be abnormal, voice warning information is sent to the student.
Compared with the prior art, the student behavior remote monitoring method and the system have the advantages that the student is shot, so that the video of the student in a preset time period is obtained, the video is subjected to image extraction processing, a plurality of images are obtained, pixel correlation information between any two adjacent images is obtained after the images are preprocessed, the two adjacent images are determined to serve as a target image pair according to the pixel correlation information, image similarity information between the two images included in the target image pair is obtained, whether the behavior of the student is normal or not is judged according to the image similarity information, and corresponding warning operation is carried out according to the judgment result; therefore, the student behavior remote monitoring method and the student behavior remote monitoring system can carry out video shooting on a student and extract a plurality of corresponding images, determine a corresponding target image pair according to the pixel correlation information of two adjacent preprocessed images, finally judge whether the behavior of the student is normal or not according to the image similarity between the two images contained in the target image pair, and carry out corresponding warning operation.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a remote monitoring method for student behavior provided by the present invention.
Fig. 2 is a schematic structural diagram of a student behavior remote monitoring system provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Fig. 1 is a schematic flow chart of a remote monitoring method for student behavior according to an embodiment of the present invention. The student behavior remote monitoring method comprises the following steps:
step S1, shooting a student to obtain a video of the student in a preset time period, and performing image extraction processing on the video to obtain a plurality of images;
step S2, after preprocessing a plurality of images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information;
and step S3, acquiring image similarity information between two images included in the target image pair, judging whether the behavior of the student is normal according to the image similarity information, and performing corresponding warning operation according to the judgment result.
The beneficial effects of the above technical scheme are: the remote monitoring method for the student behaviors can carry out video shooting on a student and extract a plurality of corresponding images, determine a corresponding target image pair according to the pixel correlation information of two adjacent preprocessed images, finally judge whether the student behaviors are normal or not according to the image similarity between the two images contained in the target image pair, and carry out corresponding warning operation.
Preferably, in step S1, capturing a student to obtain a video of the student within a preset time period, and performing image extraction processing on the video to obtain a plurality of images specifically includes:
step S101, carrying out panoramic shooting on the student so as to obtain a video of the student in the preset time period;
step S102, according to a preset time interval and a forward playing time sequence of the video, image extraction processing is carried out on the video, and therefore a plurality of images are obtained.
The beneficial effects of the above technical scheme are: by sampling and extracting a plurality of images from the shot video, the calculation amount of subsequent image processing can be greatly reduced, and the appropriate images can be conveniently selected for processing according to actual needs, so that the flexibility and controllability of student behavior monitoring are improved.
Preferably, in step S2, after preprocessing a plurality of the images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information specifically includes:
step S201, performing Kalman filtering noise reduction processing and image pixel smoothing processing on a plurality of images in sequence;
step S202, dividing each image into N rectangular image sub-regions with the same area, and determining a pixel linear correlation coefficient between any two adjacent images according to the following formula (1):
Figure BDA0002708883220000091
in the above formula (1), R (a, b) represents a pixel linear correlation coefficient between the adjacent images a and b, SiRepresenting pixel chrominance values, G, of the ith rectangular image sub-area of image biPixel chrominance values, F, of the ith rectangular image sub-region representing image aiThe texture value of the pixel, K, representing the ith rectangular image sub-region of image biThe texture value of the pixel of the ith rectangular image sub-region of the image a is represented, theta represents a preset chroma weight value and takes a value of 0.4, delta represents a preset texture weight value and takes a value of 0.6, and X represents a common correction coefficient of the image a and the image b and takes a value of [0.7, 0.9]；
Step S203, comparing the pixel linear correlation coefficient R (a, b) between the adjacent images a and b with a preset pixel linear correlation threshold, and if the pixel linear correlation coefficient R (a, b) is greater than or equal to the preset pixel linear correlation threshold, taking the current adjacent image a and image b as the target image pair, otherwise, repeating the step S202 to calculate the pixel linear correlation coefficient between the next group of adjacent two images until the calculated pixel linear correlation coefficient is greater than or equal to the preset pixel linear correlation threshold.
The beneficial effects of the above technical scheme are: by performing Kalman filtering noise reduction processing and image pixel smoothing processing on the image, interference information in the image can be effectively removed, so that the accuracy of subsequent image processing is improved; in addition, the pixel linear correlation coefficient between any two adjacent images is obtained through the calculation of the formula (1), and the relevance determination of the image pixel characteristics of the two adjacent images can be carried out on the image pixel chromaticity and the image pixel texture level, so that the reliability and the objectivity of determining the target image pair are improved.
Preferably, in step S3, the acquiring image similarity information between two images included in the target image pair, determining whether the behavior of the student is normal according to the image similarity information, and performing corresponding warning operation according to the determination result specifically includes:
step S301, determining an image similarity value between two images included in the target image pair according to the following formula (2):
Figure BDA0002708883220000101
in the above formula (2), sim represents an image similarity value between two images included in the target image pair, MdRepresenting the number Q of rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to one image in the target image paircThe number U of the rectangular image sub-areas containing the student body related pixels in the N rectangular image sub-areas corresponding to the other image in the target image pair is shownjA pixel texture value, T, of a jth rectangular image sub-region of the rectangular image sub-region representing that one image of the target image pair contains the student's body-related pixelsvThe texture value of the pixel of the v-th rectangular image sub-area of the rectangular image sub-area which represents that the other image in the target image pair contains the pixel related to the student body is epsilon, the epsilon represents a first texture compensation coefficient and the value of the first texture compensation coefficient is [0.1, 0.15 ]]And beta represents a second texture compensation coefficient and takes a value of [0.05, 0.15 ]]；
Step S302, comparing the image similarity value sim with a preset image similarity threshold, if the image similarity value sim is greater than or equal to the preset image similarity threshold, judging that the behavior of the student is abnormal, otherwise, judging that the behavior of the student is normal;
step S303, when the behavior of the student is judged to be abnormal, voice warning information is sent to the student.
The beneficial effects of the above technical scheme are: when the student makes abnormal behaviors, corresponding differences can occur between two adjacent images on a pixel level, the image similarity value between the two images is obtained through calculation of the formula (2), and the pixel differences between the two adjacent images can be quantitatively evaluated, so that various different types of abnormal behaviors made by the student can be conveniently and accurately screened out subsequently.
Fig. 2 is a schematic structural diagram of a remote monitoring system for student behavior according to an embodiment of the present invention. The remote monitoring system for the student behaviors comprises a video shooting module, an image extraction module, a target image pair determination module, a student behavior state judgment module and a warning operation module; wherein the content of the first and second substances,
the video shooting module is used for shooting a student so as to obtain a video of the student within a preset time period;
the image extraction module is used for carrying out image extraction processing on the video so as to obtain a plurality of images;
the target image pair determining module is used for preprocessing a plurality of images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information;
the student behavior state judging module is used for acquiring image similarity information between two images contained in the target image pair and judging whether the behavior of the student is normal or not according to the image similarity information;
the warning operation module is used for carrying out corresponding warning operation according to the judgment result.
The beneficial effects of the above technical scheme are: the student behavior remote monitoring system can carry out video shooting on students and extract a plurality of corresponding images, determines corresponding target image pairs according to the pixel correlation information of two adjacent preprocessed images, judges whether behaviors of the students are normal or not according to the image similarity between the two images contained in the target image pairs, and carries out corresponding warning operation.
Preferably, the video shooting module shoots the student, so as to obtain the video of the student in the preset time period specifically includes:
carrying out panoramic shooting on the student so as to obtain a video of the student within the preset time period;
and the number of the first and second groups,
the image extraction module performs image extraction processing on the video, so that obtaining a plurality of images specifically comprises:
and carrying out image extraction processing on the video according to a preset time interval and a forward playing time sequence of the video, thereby obtaining a plurality of images.
The beneficial effects of the above technical scheme are: by sampling and extracting a plurality of images from the shot video, the calculation amount of subsequent image processing can be greatly reduced, and the appropriate images can be conveniently selected for processing according to actual needs, so that the flexibility and controllability of student behavior monitoring are improved.
Preferably, the step of, after the target image pair determining module preprocesses the plurality of images, acquiring pixel correlation information between any two adjacent images, and determining, according to the pixel correlation information, two adjacent images as a target image pair specifically includes:
sequentially carrying out Kalman filtering noise reduction processing and image pixel smoothing processing on a plurality of images;
dividing each image into N rectangular image sub-areas with the same area, and determining the pixel linear correlation coefficient between any two adjacent images according to the following formula (1):
Figure BDA0002708883220000121
in the above formula (1), R (a, b) represents a pixel linear correlation coefficient between the adjacent images a and b, SiRepresenting pixel chrominance values, G, of the ith rectangular image sub-area of image biPixel chrominance values, F, of the ith rectangular image sub-region representing image aiThe texture value of the pixel, K, representing the ith rectangular image sub-region of image biThe texture value of the pixel of the ith rectangular image sub-region of the image a is represented, theta represents a preset chroma weight value and takes a value of 0.4, delta represents a preset texture weight value and takes a value of 0.6, and X represents a common correction coefficient of the image a and the image b and takes a value of [0.7, 0.9]；
Comparing the linear pixel correlation coefficient R (a, b) between the adjacent images a and b with a preset linear pixel correlation threshold, if the linear pixel correlation coefficient R (a, b) is greater than or equal to the preset linear pixel correlation threshold, taking the current adjacent images a and b as the target image pair, otherwise, repeating the step S202 to calculate the linear pixel correlation coefficient between the next group of adjacent images until the calculated linear pixel correlation coefficient is greater than or equal to the preset linear pixel correlation threshold.
The beneficial effects of the above technical scheme are: by performing Kalman filtering noise reduction processing and image pixel smoothing processing on the image, interference information in the image can be effectively removed, so that the accuracy of subsequent image processing is improved; in addition, the pixel linear correlation coefficient between any two adjacent images is obtained through the calculation of the formula (1), and the relevance determination of the image pixel characteristics of the two adjacent images can be carried out on the image pixel chromaticity and the image pixel texture level, so that the reliability and the objectivity of determining the target image pair are improved.
Preferably, the student behavior state determination module obtains image similarity information between two images included in the target image pair, and determines whether the behavior of the student is normal according to the image similarity information specifically includes:
determining an image similarity value between two images comprised by the target image pair according to the following formula (2):
Figure BDA0002708883220000131
in the above formula (2), sim represents an image similarity value between two images included in the target image pair, MdRepresenting the number Q of rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to one image in the target image paircThe number U of the rectangular image sub-areas containing the student body related pixels in the N rectangular image sub-areas corresponding to the other image in the target image pair is shownjA pixel texture value, T, of a jth rectangular image sub-region of the rectangular image sub-region representing that one image of the target image pair contains the student's body-related pixelsvThe texture value of the pixel of the v-th rectangular image sub-area of the rectangular image sub-area which represents that the other image in the target image pair contains the pixel related to the student body is epsilon, the epsilon represents a first texture compensation coefficient and the value of the first texture compensation coefficient is [0.1, 0.15 ]]And beta represents a second texture compensation coefficient and takes a value of [0.05, 0.15 ]]；
Comparing the image similarity value sim with a preset image similarity threshold, if the image similarity value sim is greater than or equal to the preset image similarity threshold, judging that the behavior of the student is abnormal, otherwise, judging that the behavior of the student is normal;
and the number of the first and second groups,
the warning operation module performs corresponding warning operation according to the judgment result, and specifically comprises:
when the behavior of the student is judged to be abnormal, voice warning information is sent to the student.
The beneficial effects of the above technical scheme are: when the student makes abnormal behaviors, corresponding differences can occur between two adjacent images on a pixel level, the image similarity value between the two images is obtained through calculation of the formula (2), and the pixel differences between the two adjacent images can be quantitatively evaluated, so that various different types of abnormal behaviors made by the student can be conveniently and accurately screened out subsequently.
As can be seen from the content of the above embodiment, the remote monitoring method and system for student behavior obtains a video of a student in a preset time period by shooting the student, performs image extraction processing on the video to obtain a plurality of images, preprocesses the plurality of images to obtain pixel correlation information between any two adjacent images, determines two adjacent images as a target image pair according to the pixel correlation information, obtains image similarity information between two images included in the target image pair, determines whether the student behavior is normal according to the image similarity information, and performs corresponding warning operation according to the result of the determination; therefore, the student behavior remote monitoring method and the student behavior remote monitoring system can carry out video shooting on a student and extract a plurality of corresponding images, determine a corresponding target image pair according to the pixel correlation information of two adjacent preprocessed images, finally judge whether the behavior of the student is normal or not according to the image similarity between the two images contained in the target image pair, and carry out corresponding warning operation.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (8)

1. The student behavior remote monitoring method is characterized by comprising the following steps:
step S1, shooting a student to obtain a video of the student in a preset time period, and performing image extraction processing on the video to obtain a plurality of images;
step S2, after preprocessing a plurality of images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information;
and step S3, acquiring image similarity information between two images included in the target image pair, judging whether the behavior of the student is normal according to the image similarity information, and performing corresponding warning operation according to the judgment result.
2. The student behavior remote monitoring method according to claim 1, wherein:
in step S1, capturing a student to obtain a video of the student within a preset time period, and performing image extraction processing on the video to obtain a plurality of images specifically includes:
step S101, carrying out panoramic shooting on the students so as to obtain videos of the students in the preset time period;
step S102, according to a preset time interval and a forward playing time sequence along the video, carrying out image extraction processing on the video, and thus obtaining a plurality of images.
3. The student behavior remote monitoring method according to claim 2, wherein:
in step S2, after preprocessing the images, obtaining pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information specifically includes:
step S201, performing Kalman filtering noise reduction processing and image pixel smoothing processing on a plurality of images in sequence;
step S202, dividing each image into N rectangular image sub-regions with the same area, and determining a pixel linear correlation coefficient between any two adjacent images according to the following formula (1):
Figure FDA0002708883210000021
in the above formula (1), R (a, b) represents a pixel linear correlation coefficient between the adjacent images a and b, SiRepresenting pixel chrominance values, G, of the ith rectangular image sub-area of image biPixel chrominance values, F, of the ith rectangular image sub-region representing image aiThe texture value of the pixel, K, representing the ith rectangular image sub-region of image biThe texture value of the pixel of the ith rectangular image sub-region of the image a is represented, theta represents a preset chroma weight value and takes a value of 0.4, delta represents a preset texture weight value and takes a value of 0.6, and X represents a common correction coefficient of the image a and the image b and takes a value of [0.7, 0.9]；
Step S203, comparing the pixel linear correlation coefficient R (a, b) between the adjacent images a and b with a preset pixel linear correlation threshold, if the pixel linear correlation coefficient R (a, b) is greater than or equal to the preset pixel linear correlation threshold, using the current adjacent image a and image b as the target image pair, otherwise, repeating the step S202 to calculate the pixel linear correlation coefficient between the next group of adjacent two images until the calculated pixel linear correlation coefficient is greater than or equal to the preset pixel linear correlation threshold.
4. A student behaviour remote monitoring method as claimed in claim 3, characterised in that:
in step S3, acquiring image similarity information between two images included in the target image pair, determining whether the behavior of the student is normal according to the image similarity information, and performing corresponding warning operation according to the determination result specifically includes:
step S301, determining an image similarity value between two images included in the target image pair according to the following formula (2):
Figure FDA0002708883210000031
in the above formula (2), sim represents an image similarity value between two images included in the target image pair, MdRepresenting the number Q of rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to one image in the target image paircRepresenting the number, U, of the rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to the other image in the target image pairjA pixel texture value, T, of a jth rectangular image sub-region of the rectangular image sub-region representing that one of the target image pair contains the student body related pixelvThe texture value of the pixel of the v-th rectangular image sub-area of the rectangular image sub-area which represents that the other image in the target image pair contains the pixel related to the student body is epsilon, the epsilon represents a first texture compensation coefficient, and the value of epsilon is [0.1, 0.15 ]]And beta represents a second texture compensation coefficient and takes a value of [0.05, 0.15 ]]；
Step S302, comparing the image similarity value sim with a preset image similarity threshold, if the image similarity value sim is greater than or equal to the preset image similarity threshold, judging that the behavior of the student is abnormal, otherwise, judging that the behavior of the student is normal;
step S303, when the behavior of the student is judged to be abnormal, voice warning information is sent to the student.
5. The remote monitoring system for the student behaviors is characterized by comprising a video shooting module, an image extraction module, a target image pair determination module, a student behavior state judgment module and a warning operation module; wherein the content of the first and second substances,
the video shooting module is used for shooting a student so as to obtain a video of the student in a preset time period;
the image extraction module is used for carrying out image extraction processing on the video so as to obtain a plurality of images;
the target image pair determining module is used for preprocessing a plurality of images, acquiring pixel correlation information between any two adjacent images, and determining two adjacent images as a target image pair according to the pixel correlation information;
the student behavior state judging module is used for acquiring image similarity information between two images contained in the target image pair and judging whether the behavior of the student is normal or not according to the image similarity information;
and the warning operation module is used for carrying out corresponding warning operation according to the judgment result.
6. The student behavior remote monitoring system of claim 5, wherein:
the video shooting module shoots students, so that the video of the students in the preset time period is obtained, and the video shooting module specifically comprises the following steps:
carrying out panoramic shooting on the students so as to obtain videos of the students in the preset time period;
and the number of the first and second groups,
the image extraction module performs image extraction processing on the video, so as to obtain a plurality of images specifically comprises:
and carrying out image extraction processing on the video according to a preset time interval and a forward playing time sequence of the video, thereby obtaining a plurality of images.
7. The student behavior remote monitoring system of claim 6, wherein:
the target image pair determining module obtains pixel correlation information between any two adjacent images after preprocessing the plurality of images, and determining two adjacent images as a target image pair according to the pixel correlation information specifically includes:
sequentially carrying out Kalman filtering noise reduction processing and image pixel smoothing processing on a plurality of images; dividing each image into N rectangular image sub-areas with the same area, and determining a pixel linear correlation coefficient between any two adjacent images according to the following formula (1):
Figure FDA0002708883210000041
in the above formula (1), R (a, b) represents a pixel linear correlation coefficient between the adjacent images a and b, SiRepresenting pixel chrominance values, G, of the ith rectangular image sub-area of image biPixel chrominance values, F, of the ith rectangular image sub-region representing image aiThe texture value of the pixel, K, representing the ith rectangular image sub-region of image biThe texture value of the pixel of the ith rectangular image sub-region of the image a is represented, theta represents a preset chroma weight value and takes a value of 0.4, delta represents a preset texture weight value and takes a value of 0.6, and X represents a common correction coefficient of the image a and the image b and takes a value of [0.7, 0.9]；
And comparing the pixel linear correlation coefficient R (a, b) between the adjacent images a and b with a preset pixel linear correlation threshold, if the pixel linear correlation coefficient R (a, b) is greater than or equal to the preset pixel linear correlation threshold, taking the current adjacent image a and image b as the target image pair, otherwise, repeating the step S202 to calculate the pixel linear correlation coefficient between the next group of adjacent two images until the calculated pixel linear correlation coefficient is greater than or equal to the preset pixel linear correlation threshold.
8. The student behavior remote monitoring system of claim 7, wherein:
the student behavior state judgment module acquires image similarity information between two images included in the target image pair, and judges whether the behavior of the student is normal specifically according to the image similarity information:
determining an image similarity value between two images comprised by the target image pair according to the following formula (2):
Figure FDA0002708883210000051
in the above formula (2), sim represents an image similarity value between two images included in the target image pair, MdRepresenting the number Q of rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to one image in the target image paircRepresenting the number, U, of the rectangular image sub-regions containing the student body related pixels in the N rectangular image sub-regions corresponding to the other image in the target image pairjA pixel texture value, T, of a jth rectangular image sub-region of the rectangular image sub-region representing that one of the target image pair contains the student body related pixelvThe texture value of the pixel of the v-th rectangular image sub-area of the rectangular image sub-area which represents that the other image in the target image pair contains the pixel related to the student body is epsilon, the epsilon represents a first texture compensation coefficient, and the value of epsilon is [0.1, 0.15 ]]And beta represents a second texture compensation coefficient and takes a value of [0.05, 0.15 ]](ii) a Comparing the image similarity value sim with a preset image similarity threshold, if the image similarity value sim is greater than or equal to the preset image similarity threshold, judging that the behavior of the student is abnormal, otherwise, judging that the behavior of the student is normal;
and the number of the first and second groups,
the warning operation module carries out corresponding warning operation according to the judgment result, and specifically comprises the following steps: and when the behavior of the student is judged to be abnormal, voice warning information is sent to the student.
CN202011048828.6A 2020-09-29 2020-09-29 Student behavior remote monitoring method and system Pending CN112200036A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114124908A (en) * 2021-08-16 2022-03-01 沭阳林冉塑业有限公司 Control method for data transmission in equipment production based on artificial intelligence

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114124908A (en) * 2021-08-16 2022-03-01 沭阳林冉塑业有限公司 Control method for data transmission in equipment production based on artificial intelligence

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