CN112087603A - Intelligent examination room supervision method - Google Patents
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Abstract
The application relates to the field of internet, and discloses a smart examination room supervision method, which comprises the following steps: acquiring basic examination information; carrying out fault detection on the examination room equipment in each examination room to obtain a fault detection result of the examination room equipment; generating examination room examination information based on the examination basic information according to the examination room equipment fault detection result; authenticating invigilators and examinees based on examination information of the examination room; judging whether the examination is disqualified or not according to the head accumulative movement distance and the body accumulative movement distance of the examinee by utilizing the head video image of the examinee and the image of the corresponding position area in the video image of the examination room corresponding to the examination terminal of the examinee, and if so, outputting disqualified alarm information. This application can supervise the act of making an appointment of examination room more high-efficiently, improves the examination fairness.
Description
Technical Field
The application relates to the technical field of internet, in particular to the technical field of network education.
Background
In recent years, with the development of educational informatization, networked examination rooms have become increasingly popular.
However, at present, some problems still exist in the networked examination room, such as: the manual supervision efficiency of the discipline behaviors of the examination hall is low, and the examination is unfair; for another example, the traditional manual verification of identity cards and examination cards has low efficiency and high error rate; also for example, examination room equipment is not effectively tested, equipment failure is likely to affect the examination schedule of the examinee, and the like.
Disclosure of Invention
The application aims to provide an intelligent examination room supervision method, which can be used for more efficiently supervising the discipline behavior of the examination room, improving the examination fairness and more efficiently and accurately checking the identity of an examinee and detecting the equipment fault of the examination room.
The application discloses wisdom examination room supervision method contains: acquiring basic examination information;
carrying out fault detection on the examination room equipment in each examination room to obtain a fault detection result of the examination room equipment;
generating examination room examination information based on the examination basic information according to the examination room equipment fault detection result;
authenticating invigilators and examinees based on examination information of the examination room;
judging whether the examination is disqualified or not according to the head accumulative movement distance and the body accumulative movement distance of the examinee by utilizing the head video image of the examinee and the image of the corresponding position area in the video image of the examination room corresponding to the examination terminal of the examinee, and if so, outputting disqualified alarm information.
In a preferred embodiment, the step of determining whether an examination violation occurs according to the head cumulative moving distance and the body cumulative moving distance of the examinee by using the head video image of the examinee and the image of the corresponding position area in the video image of the examination room corresponding to the examination terminal of the examinee further includes the following substeps:
calibrating a video image of an examination room into a plurality of areas in advance, wherein each area corresponds to one examination terminal, and storing the one-to-one correspondence relationship between the examination terminals and the areas;
periodically acquiring a head video image of an examinee corresponding to each examination terminal;
calculating the head accumulated moving distance of each examinee in a preset time interval according to the head video image of each examinee, and further acquiring the video image of the examination room in a preset time length when the head accumulated moving distance of the examinee in the preset time interval is larger than a preset threshold value;
according to a one-to-one correspondence relationship between the pre-stored examination terminal and the region, picking up an image of a position region corresponding to the examination terminal in a video image of the examination room to calculate a body cumulative moving distance of an examinee corresponding to the examination terminal within a preset time length, and when the body cumulative moving distance is larger than a preset threshold value, calculating an examination behavior default value Q of the examinee according to the head cumulative moving distance and the body cumulative moving distance of the examinee;
and when the test behavior violation score Q of the examinee meets a preset condition, outputting a test violation alarm message.
In a preferred example, in the step of periodically acquiring the head video image of the examinee corresponding to each examination terminal, the head video image of the examinee corresponding to the examination terminal is acquired every 2s by a built-in camera of the examination terminal in the examination room.
In a preferred example, in the step of obtaining the video image of the examination room within the predetermined time duration, a frame of the video image of the examination room is captured every 0.5S within a time period of 5S, so as to calculate the cumulative movement distance of the examinee corresponding to the examination terminal within the predetermined time duration.
In a preferred embodiment, the step of calculating the accumulated head movement distance of each examinee in a predetermined time interval according to the head video image of each examinee further comprises:
calculating the center position of a human face ROI in the head video image by using a human face recognition technology, judging whether the difference value between the center position of the human face ROI and the center position of the head video image is larger than a preset threshold value or not, if so, intercepting one frame of examinee head image at intervals of 0.5s within a preset time period after the triggering time to calculate the accumulated sum of the difference values between the center position of the human face ROI in a plurality of frames of the examinee head images and the center position of the examinee head image to be used as the accumulated head moving distance of the examinee.
In a preferred example, the test behavior violation score Q is calculated by: q ═ k α + l β, where α denotes the examinee's head cumulative movement distance, β denotes the examinee's body cumulative movement distance, k denotes a head cumulative movement distance coefficient, l denotes a body cumulative movement distance coefficient, and k + l ≦ 1.
In a preferred embodiment, the predetermined condition is: { α > α 1 and β > β 1}, or { Q > Q1, and α > α 1 or β > β 1}, where α 1, β 1, and Q1 are respectively preset values.
In a preferred embodiment, the test violation alarm message includes one or any combination of the following: the ID of the examination terminal, the ID of the examinee and the ID of the position corresponding to the examination terminal.
In a preferred embodiment, the examination room examination information comprises: the one-to-one correspondence among examinees, seats, face images, identity recognition and examination papers.
The application also discloses a method for supervising the intelligent examination room, which comprises the following steps:
calibrating a video image of an examination room into a plurality of areas in advance, wherein each area corresponds to one examination terminal, and storing the one-to-one correspondence relationship between the examination terminals and the areas;
periodically acquiring a head video image of an examinee corresponding to each examination terminal;
calculating the head accumulated moving distance of each examinee in a preset time interval according to the head video image of each examinee, and further acquiring the video image of the examination room in a preset time length when the head accumulated moving distance of the examinee in the preset time interval is larger than a preset threshold value;
according to a one-to-one correspondence relationship between the pre-stored examination terminal and the region, picking up an image of a position region corresponding to the examination terminal in a video image of the examination room to calculate a body cumulative moving distance of an examinee corresponding to the examination terminal within a preset time length, and when the body cumulative moving distance is larger than a preset threshold value, calculating an examination behavior default value Q of the examinee according to the head cumulative moving distance and the body cumulative moving distance of the examinee;
and when the test behavior violation score Q of the examinee meets a preset condition, outputting a test violation alarm message.
In a preferred example, the test behavior violation score Q is calculated by: q ═ k α + l β, where α denotes the examinee's head cumulative movement distance, β denotes the examinee's body cumulative movement distance, k denotes a head cumulative movement distance coefficient, l denotes a body cumulative movement distance coefficient, and k + l ≦ 1.
In a preferred embodiment, the predetermined condition is: { α > α 1 and β > β 1}, or { Q > Q1, and α > α 1 or β > β 1}, where α 1, β 1, and Q1 are respectively preset values.
In the embodiment of the application, combine standard networking examination room needs, establish networking wisdom examination room and supervisory system thereof, distinguish in real time and indicate the examinee's violation of rules and regulations at the examination in-process to realize the intelligent supervision of examination room, and, check examinee's admission through examination room equipment fault detection, identity and face identification, not only let the examination arrangement become more intelligent, each item flow of examinee's examination is more convenient, be favorable to improving the fairness of examination room moreover.
Furthermore, the video image of the head of the examinee is collected by the examination terminal, the accumulated moving distance of the head of the examinee is calculated under specific conditions, and the corresponding relation between the pre-stored examination terminal and the region of the video image of the examination room is utilized to pertinently pick the corresponding region of the examinee with the accumulated moving distance of the head larger than the threshold value in the video image of the examination room for image analysis, so that the collection and processing amount of image data in the invigilation process are obviously reduced, and the supervision efficiency is improved.
The present specification describes a number of technical features distributed throughout the various technical aspects, and if all possible combinations of technical features (i.e. technical aspects) of the present specification are listed, the description is made excessively long. In order to avoid this problem, the respective technical features disclosed in the above summary of the invention of the present application, the respective technical features disclosed in the following embodiments and examples, and the respective technical features disclosed in the drawings may be freely combined with each other to constitute various new technical solutions (which are considered to have been described in the present specification) unless such a combination of the technical features is technically infeasible. For example, in one example, the feature a + B + C is disclosed, in another example, the feature a + B + D + E is disclosed, and the features C and D are equivalent technical means for the same purpose, and technically only one feature is used, but not simultaneously employed, and the feature E can be technically combined with the feature C, then the solution of a + B + C + D should not be considered as being described because the technology is not feasible, and the solution of a + B + C + E should be considered as being described.
Drawings
FIG. 1 is a schematic diagram of a system architecture in a method for supervising an intelligent examination room according to a first embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for supervising an intelligent examination room according to a first embodiment of the present application;
fig. 3 is a schematic flow chart of a monitoring method for an intelligent examination room according to a second embodiment of the present application.
Detailed Description
In the following description, numerous technical details are set forth in order to provide a better understanding of the present application. However, it will be understood by those skilled in the art that the technical solutions claimed in the present application may be implemented without these technical details and with various changes and modifications based on the following embodiments.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
A first embodiment of the present application relates to an intelligent examination room monitoring method, as shown in fig. 1-2, the intelligent examination room monitoring method of the present embodiment can be implemented based on a networked intelligent examination room monitoring system, which includes: the system comprises a web page terminal, a server and an examination room internal system arranged in each examination room, and each examination room internal system can communicate with the web page terminal through the server.
Wherein, each examination room internal system contains: switch, examination room camera, examination room terminal, electron class tablet, entrance guard and a plurality of examination terminal, wherein, examination room camera, examination room terminal, electron class tablet, entrance guard and every examination terminal all communicate with the switch, and the switch and the server communication of every examination room. In other words, each test room internal system is connected with the same switch.
The following further exemplarily describes the components of the networked intelligent examination room monitoring system.
The webpage end is used for a manager to log in the examination room management platform, import examination basic information, issue a request instruction and realize the man-machine interaction of the information.
Note that the person login test room management platform is used for managing person login of the test room, for example, login management of examinees and proctor teachers.
Preferably, the request instruction may include, for example: automatic detection of examination room equipment failure instructions, starting of examination modes, and the like.
Further, the server refers to a data calculation and storage center, and is used for responding to the service request, performing calculation processing, and storing information and data. Preferably, the server may include: the examination room comprises an examination room device management module, an examination information management module, an information publishing module, an information storage module and the like. The examination equipment management module is used for checking and managing faults or states of examination equipment such as an examination terminal; the examination information management module is used for importing, generating and managing examination information; the information publishing module is used for downloading the examination information to each examination room terminal, an electronic class board and the like; the information storage module is used for storing information such as examination room equipment information, face image information, examination information and the like.
Furthermore, in an examination room internal system, the switch is in communication connection with examination room cameras, electronic class boards, access controls, examination room terminals and all examination terminals in the examination room, and is used for communication. The examination room terminal is equivalent to a cluster server of the examination room and is used for providing centralized management, data processing and the like for each examination terminal, namely a cloud desktop terminal. The examination terminal is a cloud desktop terminal configured in each examination room and used for examinees to answer examination questions. The examination terminal also comprises a built-in camera for identity authentication and examination behavior discrimination of examinees, and preferably, the number of the examination terminals configured in each examination room can be set or adjusted according to specific conditions. The examination room camera is arranged in the examination room and is used for shooting the panorama in the examination room, in other words, the examination room camera can cover each seat area in the examination room, and preferably, one or more examination room cameras can be arranged in each examination room. The electronic class board can be arranged in front of an examination room, for example, beside the doorway of a classroom, and is used for displaying examination information, identifying the identity of an invigilator or an examinee and controlling access control.
The following explains the networked intelligent examination room monitoring method of the networked intelligent examination room monitoring system with reference to fig. 2.
Step 110: and acquiring the basic information of the examination.
In this step, the basic information of the examination can be imported through the webpage end. Specifically, the examination basic information may include, for example: basic information such as examination subjects, the number of examinees, examination rooms and the like. The examination subjects may be, for example: linear algebra, english, computer-level examinations, ancient chinese, etc.; the examination room refers to the number of the classroom in which the examination room is located, for example: 1201 classroom, 1302 classroom, etc.
Step 120: and carrying out fault detection on the examination room equipment in each examination room to obtain a fault detection result of the examination room equipment, wherein the fault detection result is used for determining the examination room equipment which can normally work.
Specifically, in this step, the system automatically performs fault detection on the terminal equipment of the examination room and the input and output equipment matched with the terminal equipment according to the selected examination room information, wherein the fault detection equipment comprises an examination room camera, an examination room terminal, an examination terminal, an electronic class board, an entrance guard, a mouse, a keyboard, an earphone and other equipment matched with the entrance guard.
The following exemplarily describes a specific way of examination room equipment failure detection.
1) Detecting the network state of the equipment:
specifically, an examination room device detection instruction is downloaded by a webpage end, a server end sends network detection information to examination terminal devices (including examination room terminals, examination room cameras, electronic class boards and door control) in a broadcast mode, all the terminals receive the information and then feed back ID information of the carrying devices to the server, if the server receives device feedback information, the device network is normal, otherwise, the device network is abnormal, and the server records the network state of the device according to the feedback information.
2) Detecting the system state of the terminal:
specifically, based on the network state of the device, the server further sends terminal system detection information to the terminals in a normal network state, after each terminal receives the terminal system detection information, the CPU load rate, the memory usage rate and the hard disk usage rate of the system are detected, and the detection result is fed back to the server, and the server analyzes and records the fault condition of the terminal system according to the fed back information.
For example, a fault (abnormal operation) is satisfied when any one of the following conditions is satisfied: the average value of the CPU utilization rate in the preset time is higher than a%; or the memory utilization rate is higher than b%; or the residual capacity of the hard disk is lower than c%. The values set for different terminals a, b, and c are different.
Preferably, a may range from 95% or more, b may range from 50% or more, and c may range below 100M. In other embodiments, the above setting may be adjusted according to specific situations, which are not described herein.
3) Detecting examination room camera states
When the examination room camera receives monitoring information, starting shooting and intercepting a frame of shot image, feeding the image back to the server, and then, carrying out image similarity calculation on the intercepted image and a prestored original image, namely an examination room scene sample image shot in different time periods by the server, wherein if the similarity between 1 or more original images and the original images is greater than a preset threshold value, the examination room camera can normally work, and if no image information exists or other conditions such as no image information exists in returned information, the examination room camera is judged to be in fault and cannot normally work.
4) Detecting the fault of the input and output equipment matched with the terminal:
specifically, after the network and the terminal system are detected to be normal, the server side sends detection information of matched input and output equipment to the examination terminal, further sends the detection information to the examination terminal and the electronic class board through the examination terminal, and the examination room terminal performs fault analysis, summarization and feedback. Preferably, the method can be realized by the following specific modes:
first, the built-in camera detection method may be similar to the detection method of the examination room camera described above.
Further, the terminal system detects the faults of the mouse and the keyboard through the device drive, if the mouse key is detected to be accessed, the terminal system can be judged to work normally, and if the mouse key is not accessed, the terminal system is judged to have the fault. More specifically, the examination room terminal detects the device driving information through WMI, and the examination terminal detects the device driving information through an Android system configured for each terminal and feeds the device driving information back to the examination room terminal.
Further, the terminal system plays a section of audio, and generates an audio file by recording through an earphone and a microphone; secondly, the examination room terminal carries out audio similarity calculation with the original audio file according to the feedback carried audio file information, if the similarity is larger than a preset threshold value, the earphone can work normally, and otherwise, the earphone fails.
Furthermore, the terminal system starts a camera through device driving, intercepts a frame of shot image and feeds the shot image back to the examination room terminal, and the subsequent detection mode of the shot image is consistent with that of the examination room camera.
Further, the manner of detecting the built-in camera may be similar to the manner of detecting the examination room camera described above.
Furthermore, the detection mode of the identity recognizer is consistent with that of the mouse and the keyboard.
Therefore, the examination room equipment which can normally work is determined by carrying out fault detection on the examination room equipment in each examination room, and the fault equipment is recorded and warned. For example, the examination room terminals can intensively feed back the collected fault detection results of the matched input and output devices of the examination terminals and the electronic class cards to the server side, and the server side records the states of all devices to be detected in each examination room and feeds the recorded states back to the human-interface at the webpage side, so that a manager can perform device maintenance in a targeted manner.
It should be noted that, in this embodiment, the server serves as a computing master node for analyzing faults of the examination room devices, and the examination room terminals of each examination room serve as each computing node, which assists in detecting and analyzing faults of the input and output devices of the examination terminal devices, so as to implement load balancing of calculated amounts, and improve detection and communication efficiency.
Step 130: generating examination room examination information based on the examination basic information according to the examination room equipment fault detection result, wherein the examination room examination information at least comprises one of the following items or any combination thereof: examination subject, examination time, invigilation teacher, number of examinees and examinee information.
Preferably, the examination room examination information may include, for example: examination room number, examination subject, examination time, invigilator teacher, the number of examinees, examinee information, seat information corresponding to each examinee, personnel face image information, identity recognition information, examination paper and the like.
Furthermore, the examination information of the examination room may further include a one-to-one correspondence relationship between the examinees, the seats, the face images, the identification and the examination papers, and may further include a one-to-one correspondence relationship between the examinees, the seats and the examination terminals.
In this step, examination information of the examination room corresponding to each examination room can be more reasonably planned and generated according to the imported examination basic information and the examination room equipment fault detection result, namely, on the basis of factors such as examination subjects, the number of examinees in each subject, the number of examination rooms, examination positions in which each examination room can normally work and the like, and the examination information of the examination room corresponding to each examination room is downloaded to an examination room terminal and an electronic class board of each examination room.
Step 140: and authenticating invigilators and examinees based on examination information of the examination room so that the invigilators and the examinees passing the verification enter the examination room.
Specifically, identity authentication can be performed through certificates such as an identity card according to the face image information and the identity recognition information of the person in the examination information of the examination room, and the face can be collected through the camera of the examination room to perform face recognition authentication.
More specifically, in this step, the server downloads an examination starting instruction to an electronic class board of each examination room according to preset time before examination, the electronic class board immediately responds to and switches to an examination page according to the instruction, and performs service logic processing according to downloaded examination information of the examination rooms to display examination data, where the examination data includes, for example: examination subject names, examination time periods, invigilator teacher names, examinee number, examinee names, and the like; meanwhile, the electronic class card starts identity authentication and face recognition of invigilators and examinees, identity information comparison is carried out through the identity reader, face images are obtained through the camera to carry out face comparison, when the identity ID and the face ID which are compared are the same person, authentication is successful, and otherwise, identity comparison abnormity is prompted. And then, after the electronic class card successfully authenticates the person, the entrance guard switch is controlled by sending broadcast information containing the entrance guard ID and the entrance guard control instruction of the examination room in the local area network, so that the information verification of the invigilators and the examinees is realized, and the invigilators and the examinees passing the verification are allowed to enter the examination room.
Step 150: judging whether the examination is disqualified or not according to the head accumulative movement distance and the body accumulative movement distance of the examinee by utilizing the head video image of the examinee and the image of the corresponding position area in the video image of the examination room corresponding to the examination terminal of the examinee, and if so, outputting disqualified alarm information.
Preferably, this step can be realized in the following specific manner:
step 1501: calibrating a video image of an examination room into a plurality of areas in advance, wherein each area corresponds to one examination terminal, and storing the one-to-one correspondence relationship between the examination terminals and the areas;
step 1502: and periodically acquiring a head video image of the examinee corresponding to each examination terminal.
Preferably, the video image of the head of the examinee corresponding to the examination terminal can be acquired every 2s by a built-in camera of the examination terminal in the examination room.
Step 1503: and calculating the head accumulated moving distance of each examinee in a preset time interval according to the head video image of each examinee, and further acquiring the video image of the examination room in a preset time length when the head accumulated moving distance of the examinee in the preset time interval is greater than a preset threshold value.
Preferably, the predetermined time interval may be, for example: 5S, wherein a video image of the examination room is captured every, for example, 0.5S for a period of 5S.
Preferably, a face recognition technology can be applied to calculate the center position of a face ROI (region of interest) in a head video image acquired by a built-in camera of the examination terminal, and determine whether a difference between the center position of the face ROI and the center position of the head video image is greater than a predetermined threshold, if so, a frame of examinee head image is intercepted every 0.5S within a predetermined time period after a trigger time, for example, within 5S after the trigger time, so as to calculate the cumulative sum of the differences between the center position of the face ROI and the center position of the image in a plurality of frames of intercepted images, that is, whether the cumulative head movement distance α of the examinee is greater than the predetermined threshold.
It should be noted that in other embodiments of the present specification, the reference point for calculating the cumulative moving distance of the examinee's head may be other fixed positions in the image, such as the middle, upper left corner, or lower back corner of the upper edge of the examination terminal.
Step 1504: according to the pre-stored one-to-one correspondence relationship between the examination terminal and the regions, the image of the position region corresponding to the examination terminal in the video image of the examination room is extracted to calculate the body accumulative moving distance of the examinee corresponding to the examination terminal in a preset time length, and when the body accumulative moving distance is larger than a preset threshold value, the examination behavior default value Q of the examinee is calculated according to the head accumulative moving distance and the body accumulative moving distance of the examinee.
Preferably, the calculation method of the test behavior violation score Q is as follows: q ═ k α + l β, where α denotes the examinee's head cumulative movement distance, β denotes the examinee's body cumulative movement distance, k denotes a head cumulative movement distance coefficient, l denotes a body cumulative movement distance coefficient, and k + l ≦ 1
Step 1505: and when the test behavior violation score Q of the examinee meets a preset condition, outputting a test violation alarm message.
Preferably, the predetermined condition is: { α > α 1 and β > β 1}, or { Q > Q1, and α > α 1 or β > β 1}, where α 1, β 1, and Q1 are respectively preset values.
Preferably, the test violation alarm message includes one of the following or any combination thereof: the ID of the examination terminal, the ID of the examinee and the ID of the position corresponding to the examination terminal.
It should be noted that, as described above, the examination information of the examination room may further include one-to-one correspondence information between the examinees and the seats and the examination terminals, and therefore, according to any one of the information, the rest of the corresponding information may be output.
It should be noted that, in the above embodiment, the examinee examination behavior determination module of the server is a computing master node, and the examination terminals of each examination room are each computing node, so as to perform computing evaluation on the examinee examination behaviors of the examination room, and implement load balancing of the computed quantities, so as to improve computing and communication efficiency, and further improve the real-time performance of supervision. Furthermore, the server sends clock synchronization information at regular time, so that clock synchronization of communication equipment of each examination room is guaranteed, and clock synchronization of a camera of the examination room, a terminal of the examination room and a terminal of the examination room is guaranteed, and accuracy of examination behavior judgment of examinees is guaranteed.
The networked intelligent examination room monitoring method of the embodiment combines the needs of a standard networked examination room to construct a networked intelligent examination room and a monitoring system thereof, in the examination process, an examinee head image collected by a built-in camera of an examination terminal and a corresponding local area image extracted from the examination room image collected by an examination room camera are used for judging and prompting illegal actions of the examinee in real time, efficiently and accurately according to the head accumulative moving distance and the body accumulative moving distance of the examinee so as to realize the intelligent monitoring of the examination room, and the examination room is checked by the aid of examination room equipment fault detection, identity and face identification, so that examination arrangement is more intelligent, all examination flows of the examinee are more convenient, and the fairness of the examination room is improved.
The second embodiment of the present application relates to a method for supervising an intelligent examination room, the flow of which is shown in fig. 3, the method comprising the following steps:
step 310: the method comprises the steps of calibrating video images of an examination room into a plurality of areas in advance, wherein each area corresponds to one examination terminal, and storing the one-to-one correspondence relationship between the examination terminals and the areas.
Step 320: and periodically acquiring a head video image of the examinee corresponding to each examination terminal.
Preferably, the video image of the head of the examinee corresponding to the examination terminal can be acquired every 2s by a built-in camera of the examination terminal in the examination room.
Step 330: and calculating the head accumulated moving distance of each examinee in a preset time interval according to the head video image of each examinee, and further acquiring the video image of the examination room in a preset time length when the head accumulated moving distance of the examinee in the preset time interval is greater than a preset threshold value.
Preferably, the predetermined time interval may be, for example: 5S, wherein a video image of the examination room is captured every, for example, 0.5S for a period of 5S.
Preferably, a face recognition technology can be applied to calculate the center position of a face ROI (region of interest) in a head video image acquired by a built-in camera of the examination terminal, and determine whether a difference between the center position of the face ROI and the center position of the head video image is greater than a predetermined threshold, if so, a frame of examinee head image is intercepted every 0.5S within a predetermined time period after a trigger time, for example, within 5S after the trigger time, so as to calculate the cumulative sum of the differences between the center position of the face ROI and the center position of the image in a plurality of frames of intercepted images, that is, whether the cumulative head movement distance α of the examinee is greater than the predetermined threshold.
It should be noted that in other embodiments of the present specification, the reference point for calculating the cumulative moving distance of the examinee's head may be other fixed positions in the image, such as the middle, upper left corner, or lower back corner of the upper edge of the examination terminal.
Step 340: according to the pre-stored one-to-one correspondence relationship between the examination terminal and the regions, the image of the position region corresponding to the examination terminal in the video image of the examination room is extracted to calculate the body accumulative moving distance of the examinee corresponding to the examination terminal in a preset time length, and when the body accumulative moving distance is larger than a preset threshold value, the examination behavior default value Q of the examinee is calculated according to the head accumulative moving distance and the body accumulative moving distance of the examinee.
Preferably, the calculation method of the test behavior violation score Q is as follows: q ═ k α + l β, where α denotes the examinee's head cumulative movement distance, β denotes the examinee's body cumulative movement distance, k denotes a head cumulative movement distance coefficient, l denotes a body cumulative movement distance coefficient, and k + l ≦ 1.
Step 350: and when the test behavior violation score Q of the examinee meets a preset condition, outputting a test violation alarm message.
Preferably, the predetermined condition is: { α > α 1 and β > β 1}, or { Q > Q1, and α > α 1 or β > β 1}, where α 1, β 1, and Q1 are respectively preset values.
Preferably, the test violation alarm message includes one of the following or any combination thereof: the ID of the examination terminal, the ID of the examinee and the ID of the position corresponding to the examination terminal.
It should be noted that, as described above, the examination information of the examination room may further include one-to-one correspondence information between the examinees and the seats and the examination terminals, and therefore, according to any one of the information, the rest of the corresponding information may be output.
It should be noted that, in the above embodiment, the examinee examination behavior determination module of the server is a computing master node, and the examination terminals of each examination room are each computing node, so as to perform computing evaluation on the examinee examination behaviors of the examination room, and implement load balancing of the computed quantities, so as to improve computing and communication efficiency, and further improve the real-time performance of supervision. Furthermore, the server sends clock synchronization information at regular time, so that clock synchronization of communication equipment of each examination room is guaranteed, and clock synchronization of a camera of the examination room, a terminal of the examination room and a terminal of the examination room is guaranteed, and accuracy of examination behavior judgment of examinees is guaranteed.
The networked intelligent examination room monitoring method of the embodiment combines the needs of a standard networked examination room to construct a networked intelligent examination room and a monitoring system thereof, in the examination process, an examinee head image collected by a built-in camera of an examination terminal and a corresponding local area image extracted from the examination room image collected by an examination room camera are used for judging and prompting illegal actions of the examinee in real time, efficiently and accurately according to the head accumulative moving distance and the body accumulative moving distance of the examinee so as to realize the intelligent monitoring of the examination room, and the examination room is checked by the aid of examination room equipment fault detection, identity and face identification, so that examination arrangement is more intelligent, all examination flows of the examinee are more convenient, and the fairness of the examination room is improved.
Note that the first embodiment and the second embodiment can be implemented in a similar scenario, the technical details in the first embodiment can be applied to this embodiment, and the technical details in this embodiment can also be applied to the first embodiment.
It is noted that, in the present patent application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element. In the present patent application, if it is mentioned that a certain action is executed according to a certain element, it means that the action is executed according to at least the element, and two cases are included: performing the action based only on the element, and performing the action based on the element and other elements. The expression of a plurality of, a plurality of and the like includes 2, 2 and more than 2, more than 2 and more than 2.
All documents mentioned in this application are to be considered as being incorporated in their entirety into the disclosure of this application so as to be subject to modification as necessary. Further, it is understood that various changes or modifications may be made to the present application by those skilled in the art after reading the above disclosure of the present application, and such equivalents are also within the scope of the present application as claimed.
Claims (10)
1. An intelligent examination room supervision method is characterized by comprising the following steps:
acquiring basic examination information;
carrying out fault detection on the examination room equipment in each examination room to obtain a fault detection result of the examination room equipment;
generating examination room examination information based on the examination basic information according to the examination room equipment fault detection result;
authenticating invigilators and examinees based on examination information of the examination room;
judging whether the examination is disqualified or not according to the head accumulative movement distance and the body accumulative movement distance of the examinee by utilizing the head video image of the examinee and the image of the corresponding position area in the video image of the examination room corresponding to the examination terminal of the examinee, and if so, outputting disqualified alarm information.
2. The method as claimed in claim 1, wherein the step of determining whether the examination violation occurs according to the head cumulative moving distance and the body cumulative moving distance of the examinee by using the head video image of the examinee and the image of the corresponding position area in the video image of the examination room corresponding to the examination terminal of the examinee further comprises the sub-steps of:
calibrating a video image of an examination room into a plurality of areas in advance, wherein each area corresponds to one examination terminal, and storing the one-to-one correspondence relationship between the examination terminals and the areas;
periodically acquiring a head video image of an examinee corresponding to each examination terminal;
calculating the head accumulated moving distance of each examinee in a preset time interval according to the head video image of each examinee, and further acquiring the video image of the examination room in a preset time length when the head accumulated moving distance of the examinee in the preset time interval is larger than a preset threshold value;
according to a one-to-one correspondence relationship between the pre-stored examination terminal and the region, picking up an image of a position region corresponding to the examination terminal in a video image of the examination room to calculate a body cumulative moving distance of an examinee corresponding to the examination terminal within a preset time length, and when the body cumulative moving distance is larger than a preset threshold value, calculating an examination behavior default value Q of the examinee according to the head cumulative moving distance and the body cumulative moving distance of the examinee;
and when the test behavior violation score Q of the examinee meets a preset condition, outputting a test violation alarm message.
3. The method according to claim 2, wherein in the step of periodically acquiring the head video image of the examinee corresponding to each test terminal, the head video image of the examinee corresponding to the test terminal is acquired every 2s by the built-in camera of the test terminal in the examination hall.
4. The method of claim 2, wherein in the step of acquiring the video images of the test room within the predetermined time length, one frame of the video image of the test room is captured every 0.5S within a time period of 5S for calculating the cumulative body movement distance of the test taker corresponding to the test terminal within the predetermined time length.
5. The method of claim 2, wherein said step of calculating the cumulative head movement distance of each test taker over a predetermined time interval from said video image of each test taker's head further comprises:
calculating the center position of a human face ROI in the head video image by using a human face recognition technology, judging whether the difference value between the center position of the human face ROI and the center position of the head video image is larger than a preset threshold value or not, if so, intercepting one frame of examinee head image at intervals of 0.5s within a preset time period after the triggering time to calculate the accumulated sum of the difference values between the center position of the human face ROI in a plurality of frames of the examinee head images and the center position of the examinee head image to be used as the accumulated head moving distance of the examinee.
6. The method of claim 2, wherein the test activity violation score Q is calculated by: q ═ k α + l β, where α denotes the examinee's head cumulative movement distance, β denotes the examinee's body cumulative movement distance, k denotes a head cumulative movement distance coefficient, l denotes a body cumulative movement distance coefficient, and k + l ≦ 1.
7. The method of claim 2, wherein the predetermined condition is: { α > α 1 and β > β 1}, or { Q > Q1, and α > α 1 or β > β 1}, where α 1, β 1, and Q1 are respectively preset values.
8. The method of claim 2, wherein the test violation alert message comprises one or any combination of the following: the ID of the examination terminal, the ID of the examinee and the ID of the position corresponding to the examination terminal.
9. The method of claim 2, wherein the examination room test information comprises: the one-to-one correspondence among examinees, seats, face images, identity recognition and examination papers.
10. An intelligent examination room supervision method is characterized by comprising the following steps:
calibrating a video image of an examination room into a plurality of areas in advance, wherein each area corresponds to one examination terminal, and storing the one-to-one correspondence relationship between the examination terminals and the areas;
periodically acquiring a head video image of an examinee corresponding to each examination terminal;
calculating the head accumulated moving distance of each examinee in a preset time interval according to the head video image of each examinee, and further acquiring the video image of the examination room in a preset time length when the head accumulated moving distance of the examinee in the preset time interval is larger than a preset threshold value;
according to a one-to-one correspondence relationship between the pre-stored examination terminal and the region, picking up an image of a position region corresponding to the examination terminal in a video image of the examination room to calculate a body cumulative moving distance of an examinee corresponding to the examination terminal within a preset time length, and when the body cumulative moving distance is larger than a preset threshold value, calculating an examination behavior default value Q of the examinee according to the head cumulative moving distance and the body cumulative moving distance of the examinee;
and when the test behavior violation score Q of the examinee meets a preset condition, outputting a test violation alarm message.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613436A (en) * | 2020-12-28 | 2021-04-06 | 中国联合网络通信集团有限公司 | Examination cheating detection method and device |
| CN112883814A (en) * | 2021-01-26 | 2021-06-01 | 厦门大学 | Method and storage device for detecting contraband worn by double ears of examinees in online examination |
| CN113301309A (en) * | 2021-05-25 | 2021-08-24 | 上海松鼠课堂人工智能科技有限公司 | Student examination cheating behavior monitoring method and system through video monitoring |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105959624A (en) * | 2016-05-03 | 2016-09-21 | 方筠捷 | Examination room monitoring data processing method and automatic monitoring system thereof |
| CN206117880U (en) * | 2016-09-27 | 2017-04-19 | 湖南强视信息科技有限公司 | Unmanned invigilator system of many cameras to individual long -range examination |
| CN106713856A (en) * | 2016-12-15 | 2017-05-24 | 重庆凯泽科技股份有限公司 | Intelligent examination monitoring system and method |
| CN206282320U (en) * | 2016-12-02 | 2017-06-27 | 四川亚润科技有限公司 | For the electronic signing system of live identification of taking an examination |
| US10008124B1 (en) * | 2013-09-18 | 2018-06-26 | Beth Holst | Method and system for providing secure remote testing |
| CN108334846A (en) * | 2018-02-06 | 2018-07-27 | 高强 | A kind of intelligence examination hall Invigilating method and system |
| CN208849903U (en) * | 2018-07-09 | 2019-05-10 | 天津市深大立讯电子科技有限公司 | Examination intelligent monitor system and device |
| CN110290354A (en) * | 2019-07-10 | 2019-09-27 | 十里泉科技(重庆)有限公司 | A kind of with no paper examination hall examination monitoring data management method |
| CN110956074A (en) * | 2019-09-12 | 2020-04-03 | 多伦科技股份有限公司 | Intelligent analysis and study method for video of subject two examination room and study platform thereof |
| CN111353921A (en) * | 2019-08-19 | 2020-06-30 | 深圳市鸿合创新信息技术有限责任公司 | Examination management method and system and electronic equipment |
-
2020
- 2020-08-31 CN CN202010894998.XA patent/CN112087603B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10008124B1 (en) * | 2013-09-18 | 2018-06-26 | Beth Holst | Method and system for providing secure remote testing |
| CN105959624A (en) * | 2016-05-03 | 2016-09-21 | 方筠捷 | Examination room monitoring data processing method and automatic monitoring system thereof |
| CN206117880U (en) * | 2016-09-27 | 2017-04-19 | 湖南强视信息科技有限公司 | Unmanned invigilator system of many cameras to individual long -range examination |
| CN206282320U (en) * | 2016-12-02 | 2017-06-27 | 四川亚润科技有限公司 | For the electronic signing system of live identification of taking an examination |
| CN106713856A (en) * | 2016-12-15 | 2017-05-24 | 重庆凯泽科技股份有限公司 | Intelligent examination monitoring system and method |
| CN108334846A (en) * | 2018-02-06 | 2018-07-27 | 高强 | A kind of intelligence examination hall Invigilating method and system |
| CN208849903U (en) * | 2018-07-09 | 2019-05-10 | 天津市深大立讯电子科技有限公司 | Examination intelligent monitor system and device |
| CN110290354A (en) * | 2019-07-10 | 2019-09-27 | 十里泉科技(重庆)有限公司 | A kind of with no paper examination hall examination monitoring data management method |
| CN111353921A (en) * | 2019-08-19 | 2020-06-30 | 深圳市鸿合创新信息技术有限责任公司 | Examination management method and system and electronic equipment |
| CN110956074A (en) * | 2019-09-12 | 2020-04-03 | 多伦科技股份有限公司 | Intelligent analysis and study method for video of subject two examination room and study platform thereof |
Non-Patent Citations (1)
| Title |
|---|
| 李凌: "考生异常行为识别技术研究", 《淮北职业技术学院学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613436A (en) * | 2020-12-28 | 2021-04-06 | 中国联合网络通信集团有限公司 | Examination cheating detection method and device |
| CN112613436B (en) * | 2020-12-28 | 2023-08-04 | 中国联合网络通信集团有限公司 | Examination cheating detection method and device |
| CN112883814A (en) * | 2021-01-26 | 2021-06-01 | 厦门大学 | Method and storage device for detecting contraband worn by double ears of examinees in online examination |
| CN113301309A (en) * | 2021-05-25 | 2021-08-24 | 上海松鼠课堂人工智能科技有限公司 | Student examination cheating behavior monitoring method and system through video monitoring |
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|---|---|
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