CN112395950B - Classroom Intelligent Attendance Method and System - Google Patents
Classroom Intelligent Attendance Method and System Download PDFInfo
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Abstract
The application relates to an attendance technology, and discloses an intelligent attendance method and system for classrooms, which can accurately judge the attendance condition of students. The method comprises the following steps: pre-constructing a face library comprising face images of each student; triggering an electronic shift plate and a camera in the attendance checking teaching room to enter an attendance checking mode; determining an attendance student set corresponding to the attendance classroom; the electronic shift card is used for punching cards and checking work attendance of students to obtain first attendance data; periodically acquiring student images through the camera to obtain a student image set, and carrying out face recognition on each image in the student image set based on the face library to obtain second attendance data; and determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data.
Description
Technical Field
The application relates to attendance technology, in particular to classroom intelligent attendance technology.
Background
At present, the attendance mode of school class is still the mode that the teacher compares student information table and carries out artifical roll call. The attendance checking mode not only occupies a great deal of teaching time, but also seriously consumes the energy of teachers. In order to shorten the roll call time, some teachers usually only perform one or more random spot checks on the roll call, and cannot accurately judge the attendance condition of students in the whole classroom period, so that attendance errors are large, and actions such as class escaping, open class and the like are promoted to a certain extent.
Disclosure of Invention
The purpose of the application is to provide a classroom intelligent attendance method and system, which can accurately judge the attendance condition of each student in the whole class time period.
The application discloses a classroom intelligent attendance method, comprising the following steps:
pre-constructing a face library comprising face images of each student;
triggering an electronic shift plate and a camera in the attendance checking teaching room to enter an attendance checking mode;
determining an attendance student set corresponding to the attendance classroom;
the electronic class card is used for punching cards and checking work attendance of students, and first attendance data are obtained;
periodically acquiring student images through the camera to obtain a student image set, and carrying out face recognition on each image in the student image set based on the face library to obtain second attendance data;
and determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data.
In a preferred embodiment, the pre-constructing a face library, including the face image of each student, further includes:
acquiring a plurality of face images of each student;
according to the formula Scoring the ROI area of each face image of each student, wherein +.>And only one is 1, < >> Human face image ROI region resolution +.>For the resolution of the face image, f () represents the target detection model, p represents the input face image, y is the distance from the center point of the ROI area of the face image to the center point of the face image and +.>u is the laplace operator detection value of the face image and u e (0, + -infinity), ++>
And selecting the face image with the optimal score of each student to construct the face library.
In a preferred embodiment, a=0.25, β=0.05, γ=0.25,μ＝0.25；
the formula satisfies the following constraints:
in a preferred embodiment, before the electronic class board and the camera in the triggering attendance checking instruction room enter the attendance checking mode, the method further comprises:
pre-storing a classroom course schedule, wherein the classroom course schedule comprises the corresponding relation of an attendance classroom, a class period and an attendance student set;
triggering the electronic class plate and the camera in the attendance checking teaching chamber to enter an attendance mode further comprises:
determining an attendance classroom corresponding to a target class time period according to the classroom course schedule, and triggering an electronic class board and a camera in the attendance classroom to enter an attendance mode;
the determining the attendance student set corresponding to the attendance classroom further comprises:
and determining the attendance student set corresponding to the attendance classroom according to the classroom course schedule.
In a preferred embodiment, the step of punching cards for students through the electronic ban card, the step of obtaining the first attendance data further includes:
identifying a card punching student through the electronic ban card and recording card punching time to obtain first attendance data comprising the card punching student and the card punching time;
the step of carrying out face recognition on each image in the student image set based on the face library, and the step of obtaining second attendance data further comprises the following steps:
performing face recognition on each image in the student image set based on the face library;
and calculating the identified times and frequency of each student in the attendance student set according to the face recognition result to obtain second attendance data comprising the identified times and frequency of each student.
In a preferred embodiment, the determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data further includes:
comparing the card punching time of the card punching students with a target class time period, and determining the late arrival, early withdrawal or absent condition of each student in the attendance student set;
determining the class abnormal condition of each student according to the identified times and the frequency of each student, wherein the identified times are larger than a first preset threshold value, and the identified frequency is smaller than a second preset threshold value, and the students are determined to be the class abnormal condition;
and determining the attendance condition of each student in the attendance student set according to the late arrival, early withdrawal or absenteeism condition of each student and the class abnormal condition of each student.
In a preferred embodiment, the capturing the student images periodically by the camera, before obtaining the student image set, further includes:
dividing a class listening area in the attendance checking teaching room into a plurality of subareas in advance;
periodically acquiring student images by the camera, and obtaining a student image set further comprises:
and periodically scanning and shooting the student images of the lesson listening area through the camera, wherein in each acquisition period, the camera scans and shoots clockwise and then scans and shoots the plurality of subareas anticlockwise, and stops shooting a preset image for a preset time period when each subarea is scanned, so that the student image set is obtained.
The application discloses classroom intelligence attendance system includes:
the electronic class cards are used for punching cards of students to obtain first attendance data, and the cameras are used for periodically collecting student images to obtain a student image set;
the construction module is used for constructing a face library comprising face images of each student;
the triggering module is used for triggering the electronic shift plate and the camera in the attendance checking teaching room to enter an attendance checking mode;
the acquisition module is used for acquiring the first attendance data from the electronic class board and acquiring the student image set from the camera;
the face recognition module is used for recognizing the face of each image in the student image set based on the face library to obtain second attendance data;
and the determining module is used for determining an attendance student set corresponding to the attendance classroom, and determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data.
The application also discloses a classroom intelligent attendance system which comprises an electronic class board and a camera which are arranged in the classroom; and a server, the server further comprising:
a memory for storing computer executable instructions; the method comprises the steps of,
a processor for implementing steps in a method as described hereinbefore when executing said computer executable instructions.
The application also discloses a computer readable storage medium having stored therein computer executable instructions which when executed by a processor implement the steps in the method as described above.
Compared with the prior art, the embodiment of the application at least comprises the following beneficial effects:
the electronic class card and the camera are combined to realize the non-inductive attendance check, so that the attendance check condition (such as absence, late arrival, early departure, midway class escape, lying on a desk for sleeping and the like) of students can be judged more accurately.
Further, when the camera attendance is performed, based on large-scale student face images, face images meeting the optimal scoring conditions of each student are screened out according to a face image scoring formula from a plurality of aspects such as face ROI image face ratio, face confidence, face position, image definition, face position resolution and the like to form a face library of the application.
In the present application, a number of technical features are described in the specification, and are distributed in each technical solution, which makes the specification too lengthy if all possible combinations of technical features (i.e. technical solutions) of the present application are to be listed. In order to avoid this problem, the technical features disclosed in the above summary of the present application, the technical features disclosed in the following embodiments and examples, and the technical features disclosed in the drawings may be freely combined with each other to constitute various new technical solutions (these technical solutions are all regarded as being already described in the present specification) unless such a combination of technical features is technically impossible. For example, in one example, feature a+b+c is disclosed, in another example, feature a+b+d+e is disclosed, and features C and D are equivalent technical means that perform the same function, technically only by alternative use, and may not be adopted simultaneously, feature E may be technically combined with feature C, and then the solution of a+b+c+d should not be considered as already described because of technical impossibility, and the solution of a+b+c+e should be considered as already described.
Drawings
Fig. 1 is a schematic flow chart of a classroom intelligent attendance method according to a first embodiment of the present application.
Fig. 2 is a schematic diagram of a division of a plurality of sub-areas of a lecture area of an example attendance classroom.
Fig. 3 is a schematic structural diagram of a classroom intelligent attendance system according to a second embodiment of the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be understood by those skilled in the art that the claimed invention may be practiced without these specific details and with various changes and modifications from the embodiments that follow.
Description of the partial concepts:
frequency of: the number of times it is identified in a unit period.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The first embodiment of the application relates to a classroom intelligent attendance checking method, the flow of which is shown in fig. 1, and the method comprises the following steps:
in step 101, a face library is built in advance, including face images of each student.
Optionally, this step 101 may further comprise the sub-steps 101 a-101 c of:
in step 101a, acquiring a plurality of face images of each student;
in step 101b, according to the formula Scoring the ROI area of each face image of each student, wherein +_> And only one is 1, < >> Human face image ROI region resolution +.>For the resolution of the face image, f () represents the target detection model, p represents the input face image, y is the distance from the center point of the ROI area of the face image to the center point of the face image and +.>u is the laplace operator detection value of the face image and u e (0, + -infinity), ++>
In step 101c, the face image with the optimal score of each student is selected to construct the face library.
Alternatively, in the above scoring formula, α=0.25, β=0.05, γ=0.25,μ=0.25; the formula satisfies the following constraints:
in order to verify the effect, the inventor makes an experiment that, according to the scoring formula, tens of thousands of pictures of an example group are selected to form a face library of the example group, and face recognition is performed on the example group by using the face library and a plurality of image face libraries which are not selected and randomly combined, so that the result is found: compared with the face libraries of the images which are not selected and randomly combined, the face library composed of the images selected according to the scoring formula of the application has obvious improvement on the accuracy and the speed of face recognition.
Then, step 102 is entered, and the electronic shift plate and the camera in the attendance-checking teaching room are triggered to enter an attendance-checking mode.
For example, the electronic ban and camera in the attendance-teaching room may be automatically triggered to enter the attendance mode by the linkage of a classroom course schedule, or may be manually triggered by a person (e.g., by an administrator or classroom, etc.).
Optionally, before this step 102, the following steps are further included: the classroom course schedule comprises the corresponding relation of attendance classrooms, class time periods and attendance student sets.
Optionally, the classroom curriculum schedule may also include class, subject, and classroom information. Table 1 below is an example classroom curriculum schedule, and the details listed in this example are primarily for ease of understanding and are not limiting the scope of the application.
TABLE 1
Alternatively, this step 102 may be further implemented as: and determining an attendance classroom corresponding to the target class time period according to the classroom course schedule, and triggering the electronic class plate and the camera in the attendance classroom to enter an attendance mode.
Then, step 103 is entered to determine the set of attendance students corresponding to the attendance classroom.
Alternatively, this step 103 may be further implemented as: and determining an attendance student set corresponding to the attendance classroom according to the classroom course schedule.
And then, entering a step 104, and punching a card for checking in the work card by the electronic work card to obtain first checking-in data.
Alternatively, this step 104 may be further implemented as: and identifying the card punching students through the electronic ban cards and recording the card punching time to obtain first attendance data comprising the card punching students and the card punching time thereof.
The electronic ban card has various card punching and attendance checking modes. Such as, but not limited to, campus card punching (via a configured identity reader), face recognition (via a camera configured in an electronic class card and a near field face recognition algorithm/model), etc. In the embodiment of punching cards by adopting the face recognition mode, the face recognition method can be preferably based on the face library constructed in the step 101 of the application; for example, after the attendance classroom is determined, an image set of an attendance student set corresponding to the attendance classroom is obtained from the face library and sent to the electronic shift board, and the electronic shift board performs face recognition and card punching attendance based on the image set.
And then, step 105 is entered, the camera periodically collects student images to obtain a student image set, and face recognition is carried out on each image in the student image set based on the face library to obtain second attendance data.
Optionally, in the step 105, "face recognition is performed on each image in the student image set based on the face library to obtain the second attendance data" further includes the following substeps (1) and (2):
(1) carrying out face recognition on each image in the student image set based on the face library;
(2) and calculating the identified times and frequency of each student in the attendance student set according to the face recognition result to obtain second attendance data comprising the identified times and frequency of each student.
Optionally, before the step (1), the step may include: selecting face images of all students in the attendance student set determined in the step 103 from the face library; whereby the step (1) may be further implemented as: performing face recognition on each image in the student image set based on the determined face images of each student in the attendance student set; this can improve the face recognition speed.
Optionally, in step 105, a preset face recognition model may be invoked to recognize a face in an image, where the preset face recognition model may include, for example, an image input unit, a face detection unit (near-distance scene, such as, but not limited to, face-detection-return-0004, face-detection-adas-0001 models, respectively), a face alignment unit (such as, but not limited to, landmarks-regression-return-0009 models), and a face feature recognition unit (such as, but not limited to, face-recognition-return-0095 models), so as to obtain feature recognition results (as shown in table 2 below) of each image in the student image set, so that the feature recognition results are compared with face images in the face library to obtain final face recognition results.
TABLE 2
The camera is variously arranged. For example, a camera or a plurality of cameras provided on a wall surface of a lecture table side of a classroom (for example, a middle position shown in fig. 2, or a position slightly deviated from the middle position, etc.), the camera facing the lecture area; for example, a plurality of cameras may be provided on different wall surfaces or top surfaces of a classroom, and the cameras may face the lecture listening area.
Optionally, this step 105 may be preceded by the following steps:
the lecture listening area in the attendance teaching room is divided into a plurality of subareas in advance.
Optionally, the step 105 of periodically acquiring the student images by the camera to obtain the student image set is further implemented as follows: the student image of the lecture-listening area is periodically scanned and captured by the camera. In one embodiment, in each acquisition period, the camera can perform "clockwise scanning shooting and then anticlockwise scanning shooting" on the multiple sub-areas, one round of scanning shooting is completed after each time of scanning shooting is performed, for example, n rounds of scanning shooting can be performed, each round of interval time can be set according to the situation, a preset time period is reserved when each sub-area is scanned, a preset image is shot, the student image set is obtained, the images belonging to the same shooting round are labeled with the same Identification (ID), and the images belonging to different rounds are labeled with different Identifications (IDs). In another embodiment, the camera may "scan the shots counter-clockwise followed by the clockwise scan during each acquisition cycle" the plurality of sub-regions. In other embodiments, the camera may also "scan only counterclockwise or only clockwise" the plurality of sub-regions during each acquisition cycle. For example, as shown in fig. 2, an example division manner of a desk area where a student in an attendance classroom is located (i.e. a class listening area), details listed in this example are mainly for understanding, and not as a limitation on the protection scope of the application, in this example, the desk area where the student in the attendance classroom is located is divided into a, b, c, d, e, f six sub-areas, and a camera is installed in a middle position in front of the classroom, and after waiting for a attendance trigger, the camera scans a, b, c, d, e, f areas in the photographing classroom clockwise in sequence, and scans f, e, d, c, b, a areas counterclockwise again.
And then, step 106 is performed to determine the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data.
Optionally, this step 106 may further comprise the sub-steps of:
step 106a, comparing the card punching time of the card punching students with a target class time period, and determining the late arrival, early withdrawal or absent condition of each student in the attendance student set;
step 106b, determining the class abnormal condition of each student according to the identified times and the frequency of each student, wherein the students with the identified times larger than a first preset threshold value and the identified frequency smaller than a second preset threshold value are determined to be the class abnormal condition;
and step 106c, determining the attendance condition of each student in the attendance student set according to the late arrival, early withdrawal or absences of each student and the abnormal condition of each student in class.
The abnormal situations in the class can include, but are not limited to, actions such as middle course class escape, table-lying sleeping and the like.
The second embodiment of the application relates to an intelligent attendance system for classrooms, the structure of which is shown in fig. 3, and the intelligent attendance system for classrooms comprises a server, and electronic banners and cameras arranged in each classroom among N (N is more than or equal to 1).
Specifically, the electronic class card is used for punching cards of students to obtain first attendance data; the camera is used for periodically collecting student images to obtain a student image set.
Optionally, the electronic class card is configured to identify the card punch student and record the card punch time after being triggered, and obtain the first attendance data including the card punch student and the card punch time thereof.
The electronic ban card has various card punching and attendance checking modes. Such as, but not limited to, using campus cards (via a configured identity reader), face recognition (via a configured camera and near field face recognition algorithm/model), etc. In the embodiment of punching cards by adopting the face recognition mode, the face library constructed based on the application can be preferred; for example, after the attendance classroom is determined, an image set of an attendance student set corresponding to the attendance classroom is obtained from the face library and sent to the electronic class board, and the electronic class board performs face recognition based on the image set.
The camera is variously arranged. For example, a camera or a plurality of cameras provided on a wall surface of a lecture table side of a classroom (for example, a middle position shown in fig. 2, or a position slightly deviated from the middle position, etc.), the camera facing the lecture area; for example, a plurality of cameras may be provided on different wall surfaces or top surfaces of a classroom, and the cameras may face the lecture listening area.
Optionally, the camera is further configured to divide the lecture listening area within the attendance teaching room into a plurality of sub-areas in advance.
Optionally, the camera is further configured to periodically scan the student image of the lecture area by the camera after being triggered. In one embodiment, in each acquisition period, the camera can perform "clockwise scanning shooting and then anticlockwise scanning shooting" on the multiple sub-areas, one round of scanning shooting is completed after each time of scanning shooting is performed, for example, n rounds of scanning shooting can be performed, each round of interval time can be set according to the situation, a preset time period is reserved when each sub-area is scanned, a preset image is shot, the student image set is obtained, the images belonging to the same shooting round are labeled with the same Identification (ID), and the images belonging to different rounds are labeled with different Identifications (IDs). In another embodiment, the camera may "scan the shots counter-clockwise followed by the clockwise scan during each acquisition cycle" the plurality of sub-regions. In other embodiments, the camera may also "scan only counterclockwise or only clockwise" the plurality of sub-regions during each acquisition cycle. For example, as shown in fig. 2, an exemplary division of a desk area (i.e., a lecture area) where a student in a attendance classroom is located.
Further, the server also comprises a construction module, a triggering module, a face recognition module, an acquisition module and a determination module. Specifically described is:
the construction module is used for constructing a face library comprising face images of each student.
Optionally, the construction module is further configured to acquire a plurality of face images of each student according to a formula Scoring the ROI area of each face image of each student, wherein +_>And only one is 1, < >> Human face image ROI region resolution +.>For the resolution of the face image, f () represents the target detection model, p represents the input face image, y is the distance from the center point of the ROI region of the face image to the center point of the face image andu is the laplace operator detection value of the face image and u e (0, + -infinity), ++>And selecting the face image with the optimal score of each student to construct the face library.
Alternatively, in the above scoring formula, α=0.25, β=0.05, γ=0.25,μ=0.25; the formula satisfies the following constraints:
the triggering module is used for triggering the electronic shift plate and the camera in the attendance checking teaching room to enter an attendance checking mode. Optionally, the triggering module is further used for automatically triggering the electronic ban board and the camera in the attendance-checking teaching room to enter an attendance-checking mode through the linkage of a classroom course schedule, or can be manually triggered.
Optionally, the server further includes a storage module, configured to store a classroom course schedule, where the classroom course schedule includes a correspondence between an attendance classroom, a class period, and an attendance student set.
Optionally, the classroom curriculum schedule may also include class, subject, and classroom information. For example, table 1 is an example classroom curriculum schedule.
The acquisition module is used for acquiring the first attendance data from the electronic ban board and acquiring the student image set from the camera; the face recognition module is used for recognizing the face of each image in the student image set based on the face library to obtain second attendance data.
Optionally, the face recognition module is further configured to perform face recognition on each image in the student image set acquired by the camera based on the face library, and calculate the number of times and the frequency of each student in the attendance student set according to the face recognition result, so as to obtain second attendance data including the number of times and the frequency of each student identified.
Optionally, the face recognition module may acquire face images of each student in the determined attendance student set from the face library, so as to perform face recognition on each image in the student image set acquired by the camera based on the face images of each student in the determined attendance student set, and improve the face recognition speed.
Optionally, the face recognition module may also call a preset face recognition model to recognize a face in the image, where the preset face recognition model may include, for example, an image input unit, a face detection unit (near-distance scene, such as, but not limited to, a face-detection-recovery-0004 model, a face-detection-adas-0001 model, respectively), a face alignment unit (such as, but not limited to, a landmarks-recovery-0009 model), and a face feature recognition unit (such as, but not limited to, a face-recovery-0095 model, respectively), so as to obtain feature recognition results of each image in the student image set, as shown in table 2 above, and finally comparing the feature recognition results with face images in the face library to obtain a face recognition result.
Although the face recognition module in the present embodiment is disposed in the server, in other embodiments, the face recognition module may be disposed in a camera in each of the plurality of teaching rooms.
The determining module is used for determining an attendance student set corresponding to the attendance classroom and determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data.
Optionally, the determining module is further configured to determine an attendance classroom corresponding to the target class time period according to the classroom course schedule, and the triggering module is further configured to trigger the electronic class board and the camera in the corresponding attendance classroom to enter the attendance mode based on the determined attendance classroom.
Optionally, the determining module is further configured to determine an attendance student set corresponding to the attendance classroom according to the classroom course schedule.
Optionally, the determining module is further configured to compare the time of punching the card by the punching student with a target class time period, determine a late arrival, early withdrawal or absent condition of each student in the attendance student set, and determine an abnormal condition in class of each student according to the number and frequency of times each student is identified, where the number of times identified is greater than a first preset threshold and the frequency of times identified is less than a second preset threshold, determine the abnormal condition in class as the abnormal condition in class, and determine the attendance condition of each student in the attendance student set according to the late arrival, early withdrawal or absent condition of each student and the abnormal condition in class of each student. The abnormal situations in the class can include, but are not limited to, actions such as middle course class escape, table-lying sleeping and the like.
The first embodiment is a method embodiment corresponding to the present embodiment, and the technical details in the first embodiment can be applied to the present embodiment, and the technical details in the present embodiment can also be applied to the first embodiment.
It should be noted that, those skilled in the art should understand that the implementation functions of the modules shown in the embodiment of the intelligent attendance system in the classroom can be understood with reference to the related descriptions of the intelligent attendance method in the classroom. The functions of the modules shown in the embodiment of the intelligent attendance system in the classroom can be implemented by a program (executable instructions) running on a processor, and also can be implemented by a specific logic circuit. The intelligent attendance system for classrooms according to the embodiment of the application may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, and the computer software product may be stored in a storage medium, and include several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, embodiments of the present application also provide a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the method embodiments of the present application. Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable storage media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
In addition, the embodiment of the application also provides an intelligent classroom attendance system which comprises an electronic class board and a camera which are arranged in the classroom; and a server. Wherein the server includes a memory for storing computer-executable instructions, and a processor; the processor is configured to implement the steps of the method embodiments described above when executing computer-executable instructions in the memory. The processor may be a central processing unit (Central Processing Unit, abbreviated as "CPU"), other general purpose processors, digital signal processors (Digital Signal Processor, abbreviated as "DSP"), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as "ASIC"), and the like. The aforementioned memory may be a read-only memory (ROM), a random access memory (random access memory, RAM), a Flash memory (Flash), a hard disk, a solid state disk, or the like. The steps of the method disclosed in the embodiments of the present invention may be directly embodied in a hardware processor for execution, or may be executed by a combination of hardware and software modules in the processor.
It should be 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. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. In the present patent application, if it is mentioned that an action is performed according to an element, it means that the action is performed at least according to the element, and two cases are included: the act is performed solely on the basis of the element and is performed on the basis of the element and other elements. Multiple, etc. expressions include 2, 2 times, 2, and 2 or more, 2 or more times, 2 or more.
All documents mentioned in the present application are considered to be included in the disclosure of the present application in their entirety, so that they may be subject to modification if necessary. Furthermore, it should be understood that the foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present disclosure, is intended to be included within the scope of one or more embodiments of the present disclosure.
Claims (9)
1. The intelligent attendance checking method for the classrooms is characterized by comprising the following steps of:
pre-constructing a face library comprising face images of each student;
triggering an electronic shift plate and a camera in the attendance checking teaching room to enter an attendance checking mode;
determining an attendance student set corresponding to the attendance classroom;
the electronic class card is used for punching cards and checking work attendance of students, and first attendance data are obtained;
periodically acquiring student images through the camera to obtain a student image set, and carrying out face recognition on each image in the student image set based on the face library to obtain second attendance data;
determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data; wherein,
the pre-constructing a face library, including face images of each student, further includes:
acquiring a plurality of face images of each student;
according to the formulaFor each face image of each student +.>Scoring the region, wherein->And only one is 1, < >>，For face image +.>Regional resolution->For the resolution of the face image, < >>Representing the object detection model, +.>Representing the input face image, < >>For face image +.>Distance of center point of region from center point of face image and +.>Laplacian detection value for face image and +.>，；
And selecting the face image with the optimal score of each student to construct the face library.
2. The intelligent attendance method in a classroom, as claimed in claim 1, characterized in that,=0.25，=0.05=0.25，=0.1，=0.25；
the formula satisfies the following constraints:
。
3. the intelligent attendance method of claim 1, wherein before the electronic class plate and the camera in the triggering attendance room enter the attendance mode, the intelligent attendance method further comprises:
pre-storing a classroom course schedule, wherein the classroom course schedule comprises the corresponding relation of an attendance classroom, a class period and an attendance student set;
triggering the electronic class plate and the camera in the attendance checking teaching chamber to enter an attendance mode further comprises:
determining an attendance classroom corresponding to a target class time period according to the classroom course schedule, and triggering an electronic class board and a camera in the attendance classroom to enter an attendance mode;
the determining the attendance student set corresponding to the attendance classroom further comprises:
and determining the attendance student set corresponding to the attendance classroom according to the classroom course schedule.
4. The intelligent attendance method of claim 1, wherein the obtaining the first attendance data by the electronic class card punching the student further comprises:
identifying a card punching student through the electronic ban card and recording card punching time to obtain first attendance data comprising the card punching student and the card punching time;
the step of carrying out face recognition on each image in the student image set based on the face library, and the step of obtaining second attendance data further comprises the following steps:
performing face recognition on each image in the student image set based on the face library;
and calculating the identified times and frequency of each student in the attendance student set according to the face recognition result to obtain second attendance data comprising the identified times and frequency of each student.
5. The classroom intelligent attendance method of claim 4 wherein the determining the attendance status of each student in the set of attendance students from the first attendance data and the second attendance data further comprises:
comparing the card punching time of the card punching students with a target class time period, and determining the late arrival, early withdrawal or absent condition of each student in the attendance student set;
determining the class abnormal condition of each student according to the identified times and the frequency of each student, wherein the identified times are larger than a first preset threshold value, and the identified frequency is smaller than a second preset threshold value, and the students are determined to be the class abnormal condition;
and determining the attendance condition of each student in the attendance student set according to the late arrival, early withdrawal or absenteeism condition of each student and the class abnormal condition of each student.
6. The intelligent attendance method as claimed in claim 1, wherein the periodically collecting the student images by the camera, before obtaining the student image set, further comprises:
dividing a class listening area in the attendance checking teaching room into a plurality of subareas in advance;
periodically acquiring student images by the camera, and obtaining a student image set further comprises:
and periodically scanning and shooting the student images of the lesson listening area through the camera, wherein in each acquisition period, the camera scans and shoots clockwise and then scans and shoots the plurality of subareas anticlockwise, and stops shooting a preset image for a preset time period when each subarea is scanned, so that the student image set is obtained.
7. A classroom intelligence attendance system, its characterized in that includes:
the electronic class cards are used for punching cards of students to obtain first attendance data, and the cameras are used for periodically collecting student images to obtain a student image set;
the construction module is used for constructing a face library comprising face images of each student, and comprises the following steps: acquiring a plurality of face images of each student; according to the formulaFor each face image of each student +.>Scoring the region, wherein->And only one is 1, < >>，For face image +.>Regional resolution->For the resolution of the face image, < >>Representing the object detection model, +.>Representing the input face image, < >>For face image +.>Distance of center point of region from center point of face image and +.>Laplacian detection value for face image and +.>，The method comprises the steps of carrying out a first treatment on the surface of the Selecting the face image with the optimal score of each student to construct the face library;
the triggering module is used for triggering the electronic shift plate and the camera in the attendance checking teaching room to enter an attendance checking mode;
the acquisition module is used for acquiring the first attendance data from the electronic class board and acquiring the student image set from the camera;
the face recognition module is used for recognizing the face of each image in the student image set based on the face library to obtain second attendance data;
and the determining module is used for determining an attendance student set corresponding to the attendance classroom, and determining the attendance condition of each student in the attendance student set according to the first attendance data and the second attendance data.
8. The intelligent classroom attendance system is characterized by comprising an electronic class board and a camera which are arranged in a classroom; and a server, the server comprising:
a memory for storing computer executable instructions; the method comprises the steps of,
a processor for implementing the steps in the method of any one of claims 1 to 6 when executing the computer executable instructions.
9. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor implement the steps in the method of any one of claims 1 to 6.
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| CN113888116B (en) * | 2021-09-16 | 2025-04-11 | 深圳博为教育科技有限公司 | Attendance method, device and electronic equipment |
| CN114898480A (en) * | 2022-04-12 | 2022-08-12 | 广东职业技术学院 | Attendance checking method and system based on Internet of things |
| CN116611968B (en) * | 2023-06-02 | 2024-02-13 | 湖南中医药高等专科学校 | Teaching management system based on data association |
| CN117095432A (en) * | 2023-07-17 | 2023-11-21 | 北京翰博尔信息技术股份有限公司 | Intelligent analysis method and system for learning condition and attendance |
| CN118658220B (en) * | 2024-08-19 | 2024-11-26 | 福州职业技术学院 | A classroom attendance system and method based on face recognition |
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