CN113627335A - Method and device for monitoring behavior of examinee, storage medium and electronic device - Google Patents
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Abstract
The embodiment of the invention provides a method and a device for monitoring examinee behaviors, a storage medium and an electronic device, wherein the method comprises the following steps: identifying examinee information of an examinee at a target position from an examinee image at the target position of an examination scene; under the condition that the examinee information is consistent with target examinee information corresponding to a target position, classifying the examinee images to obtain a target behavior type, wherein the target behavior type is used for indicating the behavior type to which the examinee behaviors displayed in the examinee images belong; and sending first abnormal information under the condition that the target behavior type is an abnormal behavior type, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal. By the method and the device, the problem of low monitoring efficiency of the behavior of the examinee in the related technology is solved, and the effect of improving the monitoring efficiency of the behavior of the examinee is achieved.
Description
Technical Field
The embodiment of the invention relates to the field of computers, in particular to a method and a device for monitoring the behavior of an examinee, a storage medium and an electronic device.
Background
With the progress of society, educational examinations play an important role in society. How to guarantee the fairness of the examination is a problem which must be considered by the department of the examination organization. In the prior art, examination of the identity of an examinee is finished by manually checking information such as an identity card, an admission card, a reference person and the like, and the examination room discipline and the action of the examinee are monitored by manually supervising in the examination process. However, due to the development of medical treatment and the fact that the photos of the identity cards may be photos from several years ago, the change of stature and appearance are easy to be distinguished by naked eyes. In the examination process, invigilators can hardly achieve comprehensiveness and accuracy in monitoring examination room discipline and examinee behaviors through naked eyes, and the efficiency is low.
Aiming at the problem of low monitoring efficiency of the behavior of the examinee in the related technology, no effective solution is provided at present.
Disclosure of Invention
The embodiment of the invention provides a method and a device for monitoring the behavior of an examinee, a storage medium and an electronic device, which are used for at least solving the problem of low monitoring efficiency of the behavior of the examinee in the related art.
According to an embodiment of the invention, a method for monitoring the behavior of an examinee is provided, which comprises the following steps: identifying examinee information of an examinee at a target position from an examinee image at the target position of an examination scene; under the condition that the examinee information is consistent with target examinee information corresponding to the target position, classifying the examinee images to obtain target behavior types, wherein the target behavior types are used for indicating behavior types to which examinee behaviors displayed in the examinee images belong; and sending first abnormal information under the condition that the target behavior type is an abnormal behavior type, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal.
In one exemplary embodiment, the identifying the test taker information of the test taker at the target position from the test taker image at the target position of the test scene comprises: shooting the examinee image on the target position; and carrying out facial recognition on the examinee image to obtain facial features as the examinee information.
In an exemplary embodiment, before classifying the examinee image to obtain a target behavior type, the method further includes: matching the facial features with target facial features corresponding to the target positions; and determining that the test taker information is consistent with target test taker information corresponding to the target position under the condition that the facial features are matched and consistent with the target facial features.
In an exemplary embodiment, the classifying the examinee image to obtain a target behavior type includes: inputting the examinee image into a target classification model, wherein the target classification model is obtained by training an initial classification model according to a sample image marked with a behavior type; and acquiring the target behavior type output by the target classification model.
In an exemplary embodiment, before classifying the examinee image to obtain a target behavior type, the method further includes: inputting the sample image into the initial classification model; acquiring an initial behavior type output by the initial classification model; adjusting model parameters of the initial classification model according to the relation between the initial behavior type and the behavior type labeled by the sample image until the relation between the initial behavior type and the behavior type labeled by the sample image meets the training condition corresponding to the initial classification model; and determining model parameters which enable the relation between the initial behavior type and the behavior type marked by the sample image to meet the training conditions corresponding to the initial classification model as the model parameters of the target classification model to obtain the target classification model.
In one exemplary embodiment, after identifying the examinee information of the examinee on the target position from the examinee image on the target position of the examination scene, the method further comprises: and sending second abnormal information under the condition that the examinee information is inconsistent with the target examinee information corresponding to the target position, wherein the second abnormal information is used for prompting that the identity of the examinee at the target position is abnormal.
In one exemplary embodiment, before identifying test taker information for a test taker at a target location of an examination scene from an image of the test taker at the target location, the method further comprises: acquiring a scene image of the test scene; positioning each test position from the scene image to obtain a plurality of target positions included in the test scene; and acquiring each piece of target test taker information corresponding to each target position in the plurality of target positions to obtain the target position and the target test taker information with corresponding relation.
According to still another embodiment of the present invention, there is also provided a device for monitoring the behavior of an examinee, including: the system comprises an identification module, a processing module and a display module, wherein the identification module is used for identifying the examinee information of an examinee at a target position from an examinee image at the target position of an examination scene; the classification module is used for classifying the examinee image to obtain a target behavior type under the condition that the examinee information is consistent with target examinee information corresponding to the target position, wherein the target behavior type is used for indicating a behavior type to which an examinee behavior displayed in the examinee image belongs; the first sending module is used for sending first abnormal information under the condition that the target behavior type is an abnormal behavior type, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal.
According to a further embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, the examinee information of the examinee at the target position is identified from the examinee image at the target position of the examination scene; under the condition that the examinee information is consistent with target examinee information corresponding to a target position, classifying the examinee images to obtain a target behavior type, wherein the target behavior type is used for indicating the behavior type to which the examinee behaviors displayed in the examinee images belong; sending first abnormal information when the target behavior type is an abnormal behavior type, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal, that is, the examinee information of the examinee at the target position is recognized from the examinee image at the target position in the examination scene by image recognition, if the examinee information is consistent with the target examinee information corresponding to the target position, classifying the examinee images so as to identify the target behavior type of the examinee, if the target behavior type is an abnormal behavior type, first abnormal information is sent out, so that the examination behavior of the examinee on the examination room is automatically monitored and behavior abnormal is prompted by using an image recognition technology, therefore, the problem that the monitoring efficiency of the behavior of the examinee is low in the related technology is solved, and the effect of improving the monitoring efficiency of the behavior of the examinee is achieved.
Drawings
FIG. 1 is a block diagram of a mobile terminal hardware structure of a method for monitoring test taker behavior according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of monitoring test taker behavior according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a process for monitoring test taker behavior according to an embodiment of the present invention;
fig. 4 is a block diagram of a device for monitoring the behavior of a test taker according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking the example of running on a mobile terminal, fig. 1 is a block diagram of a mobile terminal hardware structure of a method for monitoring examinee behaviors according to an embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 can be used for storing computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the method for monitoring the behavior of the examinee in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for monitoring the behavior of a test taker is provided, and fig. 2 is a flowchart of the method for monitoring the behavior of a test taker according to the embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, identifying examinee information of examinees on a target position from examinee images on the target position of an examination scene;
step S204, under the condition that the examinee information is consistent with target examinee information corresponding to the target position, classifying the examinee images to obtain target behavior types, wherein the target behavior types are used for indicating behavior types to which examinee behaviors displayed in the examinee images belong;
step S206, when the target behavior type is an abnormal behavior type, sending out first abnormal information, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal.
Through the steps, the examinee information of the examinee at the target position is identified from the examinee image at the target position of the examination scene through image identification, if the examinee information is consistent with the target examinee information corresponding to the target position, the examinee image is classified, so that the target behavior type of the examinee is identified, if the target behavior type is an abnormal behavior type, first abnormal information is sent, so that the examination behavior of the examinee on the examination room is automatically monitored and behavior abnormal is prompted by using an image identification technology, therefore, the problem that the monitoring efficiency of the examinee behavior in the related technology is low is solved, and the effect of improving the monitoring efficiency of the examinee behavior is achieved.
In the technical solution provided in step S202, the target position of the test scenario may be, but is not limited to, a certain area range including each test seat or test area in the test scenario, such as: in the case of a test scenario of a written exam, the target location in the test scenario may be, but is not limited to, a range of areas that include the seat of the written exam.
Optionally, in this embodiment, after entering the target position in the examination scene, the examinee may acquire an examinee image of the examinee, where the examinee image may be, but is not limited to, a shot photo, and may also be a video image captured from a shot video.
Optionally, in this embodiment, the test taker information of the test taker may include, but is not limited to, an identification such as a biological feature (e.g., facial feature), a test number, a name, a mobile phone number, and the like of the test taker.
In an alternative embodiment, the test taker information for a test taker at a target location may be identified from an image of the test taker at the target location in the test scene, but is not limited to: shooting the examinee image on the target position; and carrying out facial recognition on the examinee image to obtain facial features as the examinee information.
Optionally, in the present embodiment, one or more image capturing devices may be deployed in the examination scene, but not limited to, to capture images of the examinee at the target position.
Alternatively, in the present embodiment, the facial features of the test taker may be recognized as the test taker information by, but not limited to, facial recognition technology.
In an optional embodiment, before classifying the examinee image to obtain the target behavior type, it may be detected whether the examinee information is consistent with the target examinee information corresponding to the target location, but not limited to, by the following means: matching the facial features with target facial features corresponding to the target positions; and determining that the test taker information is consistent with target test taker information corresponding to the target position under the condition that the facial features are matched and consistent with the target facial features.
Optionally, in the present embodiment, a feature matching technique may be employed, but is not limited to, to determine whether the facial features identified from the test taker image match the target facial features.
Alternatively, in the present embodiment, the process of identifying the facial features of the test taker from the test taker image and matching the facial features may be, but is not limited to being, performed by a trained feature matching model. The feature matching model can comprise a feature extraction part and a feature matching part, wherein an image of an examinee is input into the feature matching model, facial features of the examinee are identified from the image of the examinee by the feature extraction part, the identified facial features are matched with target facial features stored in advance by the feature matching part, and the output of the model can be used as a matching result to indicate whether the information of the examinee is consistent with the target examinee information corresponding to the target position.
In the technical solution provided in step S204 above, the behavior generated by the examinee in the examination scenario may be, but is not limited to, divided into various types according to various criteria.
Such as: one way is that the behavior of the examinee in the test scenario can be divided into a normal behavior type and an abnormal behavior type, and the normal behavior type and the abnormal behavior type can be distinguished according to the behavior characteristics of the test behavior exhibited by the examinee in the test scenario, such as: if the head orientation falls within the preset range, the test behavior can be judged to be the normal behavior type, and if the head orientation does not fall within the preset range, the test behavior can be judged to be the abnormal behavior type.
Another way may be to subdivide the test taker's behavior in the test scenario, including: turning behavior, stretching behavior, communication behavior, answering behavior and the like, and dividing the subdivided behavior types into normal behavior types and abnormal behavior types, wherein the normal behavior types can comprise answering behavior and the like, and the abnormal behavior types can comprise turning behavior, stretching behavior, communication behavior and the like.
In an alternative embodiment, the examinee images may be classified, but are not limited to, by: inputting the examinee image into a target classification model, wherein the target classification model is obtained by training an initial classification model according to a sample image marked with a behavior type; and acquiring the target behavior type output by the target classification model.
Optionally, in the present embodiment, the examination behaviors shown in the image of the examinee may be automatically classified by, but not limited to, a trained target classification model.
In an optional embodiment, before classifying the examinee image to obtain the target behavior type, the initial classification model may also be trained, but not limited to, in the following manner to obtain a target classification model: inputting the sample image into the initial classification model; acquiring an initial behavior type output by the initial classification model; adjusting model parameters of the initial classification model according to the relation between the initial behavior type and the behavior type labeled by the sample image until the relation between the initial behavior type and the behavior type labeled by the sample image meets the training condition corresponding to the initial classification model; and determining model parameters which enable the relation between the initial behavior type and the behavior type marked by the sample image to meet the training conditions corresponding to the initial classification model as the model parameters of the target classification model to obtain the target classification model.
Optionally, in this embodiment, a maximum number of model iterations may also be set, and if, in the above model training process, the relationship between the initial behavior type and the behavior type labeled by the sample image fails to meet the training condition corresponding to the initial classification model when the number of model iterations reaches a preset maximum number, the iteration process is ended, and a developer may perform model training after adjusting the hyper-parameters, the model structure, and the like of the initial classification model.
In an optional embodiment, after the examinee information of the examinee at the target position is identified from the examinee image at the target position in the examination scene, the abnormality prompt can be further performed on the condition that the examinee information is inconsistent with the target examinee information corresponding to the target position by, but not limited to: and sending second abnormal information under the condition that the examinee information is inconsistent with the target examinee information corresponding to the target position, wherein the second abnormal information is used for prompting that the identity of the examinee at the target position is abnormal.
Optionally, in this embodiment, the sent second exception information may be, but is not limited to, sent to a client corresponding to the examination scene for being checked by the invigilator, or may also be sent to a broadcasting device for broadcasting, or may also be sent to a management device of the examination for being uniformly processed by the invigilator.
Optionally, in this embodiment, the invigilator may, but is not limited to, verify the identity of the examinee at the target location according to the received second abnormality information.
In an alternative embodiment, before the examinee information of the examinee at the target position is identified from the examinee image at the target position in the examination scene, the invigilation process in the examination scene can be prepared in advance by, but not limited to: acquiring a scene image of the test scene; positioning each test position from the scene image to obtain a plurality of target positions included in the test scene; and acquiring each piece of target test taker information corresponding to each target position in the plurality of target positions to obtain the target position and the target test taker information with corresponding relation.
Optionally, in this embodiment, the target test taker information may include, but is not limited to, a frontal face image of a person, a test number, a name, a mobile phone number, and the like, which are entered by the test taker.
Optionally, in the present embodiment, a plurality of target positions included in the test scenario may be identified, but not limited to, in the form of coordinate ranges.
In the technical solution provided in step S206, the sent first abnormal information may be, but is not limited to, sent to the client corresponding to the examination scene for being checked by the proctor, or may also be sent to the broadcasting device for broadcasting, or may also be sent to the management device of the examination at this time for being processed by the proctor in a unified manner.
Optionally, in this embodiment, the client corresponding to the test scenario may be, but is not limited to, a terminal device or a mobile terminal deployed in the test scenario, and may also be a terminal device or a mobile terminal carried by an invigilator in the test scenario.
Optionally, in this embodiment, the examiner in the examination scene may perform examination action investigation or intensive monitoring on the examinee at the target position according to the received first abnormal information.
It is to be understood that the above-described embodiments are only a few, but not all, embodiments of the present invention.
The present invention will be described in detail with reference to the following examples:
in this embodiment, an anti-cheating management system is provided, where an examinee behavior in an examination scene is monitored through the anti-cheating management system, fig. 3 is a schematic diagram of a monitoring process of an examinee behavior according to an embodiment of the present invention, as shown in fig. 3, in an examination preparation stage, information may be entered into the anti-cheating management system, where the system includes: an examinee inputs personal face image and other identity information, an examination room camera system collects a background image of a standard examination room, coordinates of each seat in the examination room are located, and a plurality of target positions are obtained. At invigilating the stage, examination room camera system begins to gather examination room image in real time and invigilates, invigilates the process and can divide into two control stages: and acquiring the face image information (equivalent to the examinee information on the target position) of each seat of examinees and the face image information (equivalent to the target examinee information corresponding to the target position) of the examinee, inputting the face image information into the system, performing matching algorithm operation, and checking whether the examinee takes the test or not. And collecting key position information of examinees in each seat, performing classification algorithm operation on the examinee behaviors, and checking whether the examinee behaviors are abnormal or not. If the anti-cheating management system detects abnormal conditions in the process, the system prompts an invigilator for on-site verification, if the examinee really has cheating behaviors, corresponding measures are taken, and if the abnormal conditions are not found, the anti-cheating management system continues invigilating until the examination is finished.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a device for monitoring the behavior of a test taker is further provided, and fig. 4 is a block diagram of a structure of the device for monitoring the behavior of a test taker according to an embodiment of the present invention, as shown in fig. 4, the device includes:
the identification module 42 is used for identifying the examinee information of the examinee at the target position from the examinee image at the target position of the examination scene;
the classification module 44 is configured to classify the examinee image to obtain a target behavior type when the examinee information is consistent with target examinee information corresponding to the target position, where the target behavior type is used to indicate a behavior type to which an examinee behavior shown in the examinee image belongs;
and a first sending module 46, configured to send first exception information when the target behavior type is an exception behavior type, where the first exception information is used to prompt that an exception exists in the examination behavior of the examinee at the target location.
Through the module, the examinee information of the examinee at the target position is identified from the examinee image at the target position of the examination scene through image identification, if the examinee information is consistent with the target examinee information corresponding to the target position, the examinee image is classified, so that the target behavior type of the examinee is identified, if the target behavior type is an abnormal behavior type, first abnormal information is sent, so that the examination behavior of the examinee on the examination room is automatically monitored and behavior abnormal is prompted by using an image identification technology, therefore, the problem that the monitoring efficiency of the examinee behavior in the related technology is low is solved, and the effect of improving the monitoring efficiency of the examinee behavior is achieved.
In an alternative embodiment, the identification module comprises: a shooting unit for shooting the examinee image at the target position; and the identification unit is used for carrying out facial identification on the examinee image to obtain facial features as the examinee information.
In an optional embodiment, the apparatus further comprises: the matching module is used for matching the facial features with the target facial features corresponding to the target positions before classifying the examinee images to obtain the target behavior types; and the first determining module is used for determining that the test taker information is consistent with the target test taker information corresponding to the target position under the condition that the facial features are matched and consistent with the target facial features.
In an alternative embodiment, the classification module comprises: the input unit is used for inputting the examinee image into a target classification model, wherein the target classification model is obtained by training an initial classification model according to a sample image marked with a behavior type; and the obtaining unit is used for obtaining the target behavior type output by the target classification model.
In an optional embodiment, the apparatus further comprises: the input module is used for inputting the sample image into the initial classification model before classifying the examinee image to obtain a target behavior type; the first obtaining module is used for obtaining the initial behavior type output by the initial classification model; the adjusting module is used for adjusting the model parameters of the initial classification model according to the relationship between the initial behavior type and the behavior type labeled by the sample image until the relationship between the initial behavior type and the behavior type labeled by the sample image meets the training condition corresponding to the initial classification model; and the second determining module is used for determining model parameters which enable the relation between the initial behavior type and the behavior type marked by the sample image to meet the training conditions corresponding to the initial classification model as the model parameters of the target classification model to obtain the target classification model.
In an optional embodiment, the apparatus further comprises: the second sending module is used for sending second abnormal information under the condition that the examinee information is inconsistent with the target examinee information corresponding to the target position after the examinee information of the examinee at the target position is identified from the examinee image at the target position of the examination scene, wherein the second abnormal information is used for prompting that the identity of the examinee at the target position is abnormal.
In an optional embodiment, the apparatus further comprises: the second acquisition module is used for acquiring a scene image of an examination scene before examinee information of an examinee at a target position is identified from an examinee image at the target position of the examination scene; the positioning module is used for positioning each test position from the scene image to obtain a plurality of target positions included in the test scene; and the third acquisition module is used for acquiring each piece of target test taker information corresponding to each target position in the plurality of target positions to obtain the target position and the target test taker information with corresponding relation.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for monitoring the behavior of an examinee is characterized by comprising the following steps:
identifying examinee information of an examinee at a target position from an examinee image at the target position of an examination scene;
under the condition that the examinee information is consistent with target examinee information corresponding to the target position, classifying the examinee images to obtain target behavior types, wherein the target behavior types are used for indicating behavior types to which examinee behaviors displayed in the examinee images belong;
and sending first abnormal information under the condition that the target behavior type is an abnormal behavior type, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal.
2. The method of claim 1, wherein the identifying the test taker information of the test taker at the target location from the test taker image at the target location of the test scene comprises:
shooting the examinee image on the target position;
and carrying out facial recognition on the examinee image to obtain facial features as the examinee information.
3. The method of claim 2, wherein prior to classifying the test taker image for a target behavior type, the method further comprises:
matching the facial features with target facial features corresponding to the target positions;
and determining that the test taker information is consistent with target test taker information corresponding to the target position under the condition that the facial features are matched and consistent with the target facial features.
4. The method of claim 1, wherein the classifying the test taker image to obtain a target behavior type comprises:
inputting the examinee image into a target classification model, wherein the target classification model is obtained by training an initial classification model according to a sample image marked with a behavior type;
and acquiring the target behavior type output by the target classification model.
5. The method of claim 4, wherein prior to classifying the test taker image for a target behavior type, the method further comprises:
inputting the sample image into the initial classification model;
acquiring an initial behavior type output by the initial classification model;
adjusting model parameters of the initial classification model according to the relation between the initial behavior type and the behavior type labeled by the sample image until the relation between the initial behavior type and the behavior type labeled by the sample image meets the training condition corresponding to the initial classification model;
and determining model parameters which enable the relation between the initial behavior type and the behavior type marked by the sample image to meet the training conditions corresponding to the initial classification model as the model parameters of the target classification model to obtain the target classification model.
6. The method according to any one of claims 1 to 5, wherein after identifying test taker information of a test taker at a target position of a test scene from an image of the test taker at the target position, the method further comprises:
and sending second abnormal information under the condition that the examinee information is inconsistent with the target examinee information corresponding to the target position, wherein the second abnormal information is used for prompting that the identity of the examinee at the target position is abnormal.
7. The method of any of claims 1 to 5, wherein prior to identifying test taker information for a test taker at a target location of an examination scene from an image of the test taker at the target location, the method further comprises:
acquiring a scene image of the test scene;
positioning each test position from the scene image to obtain a plurality of target positions included in the test scene;
and acquiring each piece of target test taker information corresponding to each target position in the plurality of target positions to obtain the target position and the target test taker information with corresponding relation.
8. An examinee behavior monitoring device, comprising:
the system comprises an identification module, a processing module and a display module, wherein the identification module is used for identifying the examinee information of an examinee at a target position from an examinee image at the target position of an examination scene;
the classification module is used for classifying the examinee image to obtain a target behavior type under the condition that the examinee information is consistent with target examinee information corresponding to the target position, wherein the target behavior type is used for indicating a behavior type to which an examinee behavior displayed in the examinee image belongs;
the first sending module is used for sending first abnormal information under the condition that the target behavior type is an abnormal behavior type, wherein the first abnormal information is used for prompting that the examination behavior of the examinee on the target position is abnormal.
9. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method as claimed in any of claims 1 to 7 are implemented when the computer program is executed by the processor.
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