WO2017215315A1 - 一种教师上课考勤监控方法、系统及装置 - Google Patents

一种教师上课考勤监控方法、系统及装置 Download PDF

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Publication number
WO2017215315A1
WO2017215315A1 PCT/CN2017/078781 CN2017078781W WO2017215315A1 WO 2017215315 A1 WO2017215315 A1 WO 2017215315A1 CN 2017078781 W CN2017078781 W CN 2017078781W WO 2017215315 A1 WO2017215315 A1 WO 2017215315A1
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image
attendance
teacher
classroom
class
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PCT/CN2017/078781
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English (en)
French (fr)
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陈登杭
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杭州海康威视系统技术有限公司
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Priority to EP17812429.3A priority Critical patent/EP3471014A4/en
Priority to US16/309,079 priority patent/US11113512B2/en
Publication of WO2017215315A1 publication Critical patent/WO2017215315A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G06V40/167—Detection; Localisation; Normalisation using comparisons between temporally consecutive images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/1091—Recording time for administrative or management purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification

Definitions

  • the present application relates to the field of image processing technologies, and in particular, to a method, system and device for monitoring attendance of a teacher.
  • Attendance system is the core tool of human resource management in enterprises, schools and other units.
  • the application of attendance system is often linked to personnel performance and salary, because it is highly valued by managers at all levels.
  • attendance data is very important for students and teachers, and is an evaluation of students and teachers. An important basis.
  • an image acquisition device is generally installed in each classroom, and an image in the classroom is acquired through the image acquisition device. If the monitoring personnel wants to monitor the attendance of the A teacher, it is necessary to determine which classroom the teacher is currently attending, so as to call up the image of the classroom. According to whether there is a teacher in the image, when there is a teacher, it is also necessary to determine whether the teacher is A teacher can monitor the attendance of teachers.
  • the image of the teacher may not be in the recalled image, resulting in a judgment error; or because the image acquisition device
  • the clarity is not enough, only the magnification can tell if the teacher is in the right class.
  • the above monitoring program also needs the monitoring personnel to know each teacher, in order to determine whether the correct teacher is in class, and need to know the relevant course information of each teacher, the operability is poor, and the requirements of the monitoring personnel It is also relatively high, and it is impossible to effectively balance the accuracy of monitoring and monitoring efficiency.
  • the embodiment of the present application discloses a method, a system and a device for monitoring the attendance of a teacher.
  • the embodiment of the present application discloses a method for monitoring attendance of a teacher, wherein an image capturing device is installed in the classroom, and an image capturing area of the image capturing device includes a platform area of the classroom, and any class corresponding to the classroom is Divided into a plurality of time periods, the method includes:
  • Determining whether the image is included in the image if yes, acquiring at least one picture by the image collecting device; and for each picture, according to the image of the face to be recognized in the picture and the image in the face comparison database Similarity, determining the attendance result of the teacher corresponding to the classroom in the time period;
  • the attendance result of the teacher corresponding to the classroom in each time period is determined.
  • determining the attendance result of the teacher corresponding to the classroom in the time period includes:
  • determining the attendance result of the teacher corresponding to the classroom in the time period includes:
  • the image of the teacher who is scheduled to be in the classroom during the class in the face comparison database includes:
  • the method further includes:
  • the similarity threshold is lowered
  • the attendance of the teacher corresponding to the classroom is normal, and the attendance result of the teacher corresponding to the classroom in the time period or the class is reversed, or the face image to be recognized in each picture and the acquisition
  • the similarity threshold is raised.
  • the lowering the similarity threshold includes:
  • the raising the similarity threshold includes:
  • determining, according to the attendance result of the teacher corresponding to the classroom in each time period, determining the attendance result of the teacher corresponding to the classroom in the class time includes:
  • the type of the attendance abnormality of the teacher corresponding to the classroom in the class is determined according to the time period corresponding to the attendance abnormality.
  • the determining, according to the time period corresponding to the attendance abnormality, the type of the attendance abnormality of the teacher corresponding to the classroom in the class time includes:
  • the time period corresponding to the attendance abnormality is the first time period in the class hour, it is determined that the type of the attendance abnormality is not on time;
  • the type of the attendance abnormality is determined to be the class in advance
  • the method further includes: performing the following steps one or more times repeatedly, and determining the attendance result in the time period. Whether the number of abnormal times is greater than a preset threshold, and if so, finally determining the attendance abnormality of the teacher corresponding to the classroom in the time period:
  • the image acquired by the image collection device is acquired again, and it is determined whether the image includes a face image; if yes, at least one image is acquired by the image collection device; and for each image, according to the image
  • the similarity between the face image to be recognized and the image in the face comparison database determines whether the attendance of the teacher corresponding to the classroom is normal during the time period.
  • the method further includes:
  • the embodiment of the present application discloses a teacher attendance monitoring device, wherein an image capturing device is installed in the classroom, and an image capturing area of the image capturing device includes a platform area of the classroom, and any class corresponding to the classroom is Divided into a plurality of time periods, the device includes:
  • An acquiring module configured to acquire an image of the podium area collected by an image collection device installed in the classroom during at least one time period of the class;
  • a processing module configured to determine whether the image is included in the image; if yes, obtain at least one picture by using the image collecting device; and for each picture, compare the face image to be recognized in the picture according to the face The similarity of the images in the database, determining the attendance results of the teachers corresponding to the classroom during the time period;
  • the determining module is configured to determine the attendance result of the teacher corresponding to the classroom in the class according to the attendance result of the teacher corresponding to the classroom in each time period.
  • the processing module includes:
  • Obtaining a sub-module configured to obtain an image of a teacher in which the classroom is scheduled during the class in the face comparison database
  • a processing submodule configured to determine, for each picture, whether the similarity between the face image to be recognized in the picture and the acquired image is greater than a predetermined similarity threshold, and if yes, increase the number of recorded pictures by one;
  • a first determining submodule configured to determine whether the number of recorded pictures is greater than a set number threshold, and if yes, determining that the attendance of the teacher corresponding to the classroom is normal during the time period; otherwise, determining the teacher corresponding to the classroom in the time period The attendance is abnormal.
  • the processing module includes:
  • Obtaining a sub-module configured to obtain an image of a teacher in which the classroom is scheduled during the class in the face comparison database
  • a recognition submodule configured to determine a similarity between the face image to be recognized in each picture and the acquired image, and identify a maximum value of the similarity
  • a second determining submodule configured to determine whether a maximum value of the similarity is greater than a predetermined similarity threshold, and if yes, determining that the attendance of the teacher corresponding to the classroom is normal during the time period; otherwise, determining the time corresponding to the classroom The teacher's attendance is abnormal.
  • the obtaining sub-module is specifically configured to retrieve pre-stored course information of the classroom, where the course information records the name or number of the teacher who is scheduled for each class; The name or number of the class teacher in the class at the time of class, and the corresponding image of the class teacher is obtained in the face comparison database.
  • the device further includes:
  • a lowering module configured to: when determining the attendance abnormality of the teacher corresponding to the classroom in the time period or the class, and the attendance result of the teacher corresponding to the classroom in the time period or the class is corrected, lowering the similarity threshold;
  • the height adjustment module is configured to: when determining the time period or the class time, the attendance of the teacher corresponding to the classroom is normal, and the attendance result of the teacher corresponding to the classroom in the time period or the class is reversed, or the image to be recognized in each picture When the similarity between the face image and the acquired image is greater than the similarity threshold, the similarity threshold is raised.
  • the lowering module is specifically configured to: lower the similarity threshold according to the set first proportional coefficient; determine whether the reduced similarity threshold is lower than a preset minimum threshold, and if yes, The similarity threshold is adjusted to the lowest threshold; or
  • the height adjustment module is specifically configured to increase the similarity threshold according to the set second proportional coefficient
  • the determining module includes:
  • a judging sub-module configured to determine whether a time period of attendance abnormality exists
  • Determining a sub-module configured to: when the judgment sub-module determines that the result is no, determine that the attendance of the teacher corresponding to the classroom is normal during the class; and when the judgment sub-module determines that the result is yes, according to the time period corresponding to the attendance abnormality Determine the type of attendance abnormality of the teacher corresponding to the classroom during the class.
  • the determining sub-module is specifically configured to: when the time period corresponding to the attendance abnormality is the first time period in the class, determine that the type of the attendance abnormality is not on time; and the time period corresponding to the attendance abnormality For the last time period in the class, it is determined that the type of attendance abnormality is early class; when the time period corresponding to the attendance abnormality is neither the first time period nor the last time period in the class time, then it is determined The type of attendance exception is left midway.
  • the device further includes:
  • the execution module is configured to repeatedly perform one or more of the following steps when determining the attendance abnormality of the teacher corresponding to the classroom in the first round for any time period, and determine whether the number of times the attendance result is abnormal during the time period is If the value is greater than the preset threshold, if yes, the attendance abnormality of the teacher corresponding to the classroom is determined:
  • the image acquired by the image collection device is acquired again, and it is determined whether the image includes a face image; if yes, at least one image is acquired by the image collection device; and for each image, according to the image
  • the similarity between the face image to be recognized and the image in the face comparison database determines whether the attendance of the teacher corresponding to the classroom is normal during the time period.
  • the processing module is further configured to determine, at the beginning of each time period, whether the state of the trackball is a stationary state, and if yes, acquire the captured image. .
  • the embodiment of the present application discloses a teacher attendance attendance monitoring system, where the system includes an image collection device installed in a classroom, and the teacher attendance attendance monitoring device according to the second aspect.
  • the present application provides a storage medium, wherein the storage medium is configured to store executable program code, and the executable program code is configured to execute at the runtime as described above.
  • a teacher attendance monitoring method In the aspect, a teacher attendance monitoring method.
  • the present application provides an application, wherein the application is configured to execute a teacher attendance monitoring method according to the first aspect described above at runtime.
  • the application provides an electronic device, including:
  • processor a memory, a communication interface, and a bus
  • the processor, the memory, and the communication interface are connected by the bus and complete communication with each other;
  • the memory stores executable program code
  • the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory for performing a teacher attendance monitoring method as described in the first aspect above.
  • the embodiment of the present application provides a method, system and device for monitoring attendance of a teacher, wherein an image collection device is installed in the classroom, and an image collection area of the image collection device includes a lecture area of the classroom, and any class corresponding to the classroom is divided into For a plurality of time periods, the method includes: acquiring an image of the podium area collected by the image collection device installed in the classroom during at least one time period of the class; determining whether the image includes a face image; Yes, at least one picture is acquired by the image collecting device; for each picture, according to the similarity between the face image to be recognized in the picture and the image in the face comparison database, the teacher corresponding to the classroom in the time period is determined The attendance result of the teacher; according to the attendance result of the teacher corresponding to the classroom in each time period, the attendance result of the teacher corresponding to the classroom in the class time is determined.
  • the image acquisition device is installed in the classroom in the embodiment of the present application, and the image collection area of the image collection device includes a podium area of the classroom.
  • the image collected by the image collection device includes a face image
  • at least one picture is acquired. It is guaranteed that the face image of the teacher can be collected to ensure the correctness of the subsequent attendance judgment.
  • the image of the teacher in the classroom during the class is in the database.
  • the similarity degree is used to judge whether the attendance of the teacher in the classroom is normal during the class, so that the accuracy of the attendance monitoring of the teacher can be further ensured, and the attendance is effectively recognized by the electronic device, and the manual identification is not required by the experienced staff, thereby effectively improving the attendance. Monitors efficiency and simplifies attendance monitoring operations.
  • FIG. 1 is a flowchart of a method for monitoring attendance of a teacher in a class according to an embodiment of the present application
  • FIG. 2 is a schematic structural diagram of a teacher attendance monitoring device according to an embodiment of the present application.
  • FIG. 3 is a schematic structural diagram of a teacher attendance attendance monitoring system according to an embodiment of the present application.
  • the embodiment of the present application provides a method, system and device for monitoring the attendance of the teacher.
  • FIG. 1 is a flowchart of a method for monitoring attendance of a teacher in a class according to an embodiment of the present application, where the method may include:
  • S101 Acquire an image of the podium area collected by an image collection device installed in a classroom during at least one time period of the class.
  • the embodiment of the present application can be applied to an electronic device, and the electronic device can be any device having an identification monitoring function, for example, a server or the like.
  • an image capturing device is installed in the classroom, and the image capturing area of the image capturing device includes a podium area of the classroom. Moreover, the image capturing device and the electronic device are connected by wire or wirelessly.
  • any classroom can be monitored by an electronic device, and any class time is taken as an example to describe the method for monitoring the attendance of the teacher in the embodiment of the present application.
  • the method of the embodiment of the present application can be used to monitor the teacher attendance of each class in each classroom through an electronic device.
  • the class time and class time are fixed, so the corresponding time period for each class is also determined.
  • the class is divided into multiple time segments in the embodiment of the present application, for example, may be divided into three, four or five time periods.
  • the attendance result of the teacher corresponding to the classroom in the class is determined.
  • each class hour may be divided into the same plurality of time segments, for example, each class hour is equally divided into four time segments.
  • the electronic device may acquire an image of the podium area collected by the image collection device installed in the classroom.
  • step S102 Determine whether the image is included in the image; if yes, proceed to step S103, and if no, return to the step of acquiring the image of the podium region acquired by the image capturing device installed in the classroom in step S101.
  • the process of determining whether a face image is included in the collected image may be in the prior art, and the process in this application does not describe the process.
  • S103 Acquire at least one picture by using the image collection device. For each picture, determine a teacher corresponding to the classroom according to the similarity between the face image to be recognized in the picture and the image in the face comparison database. Attendance results.
  • the face comparison database is stored locally in the electronic device, and the face image of each teacher is saved in the face comparison database.
  • at least one picture is acquired by the image collecting device at each moment, for example, three pictures, five pictures, etc., and each picture is determined to be
  • the similarity between the recognized face image and the image of the corresponding teacher in the face comparison database if the image of the face to be recognized in a certain picture and the face of the face in the face comparison database are more similar than the image of the teacher in the classroom
  • the set similarity threshold determines that the teacher attendance recorded in the picture is normal. If at least one picture is taken at that time, record the teacher test When the number of frequently-used pictures is greater than the set number threshold, it is determined that the teacher attendance recorded in the picture is normal.
  • S104 Determine the attendance result of the teacher corresponding to the classroom in the class according to the attendance result of the teacher corresponding to the classroom in each time period.
  • a class time includes a plurality of time periods.
  • the attendance result of the teacher corresponding to the classroom in the class time may be determined according to the attendance result of the teacher corresponding to the classroom in each time period.
  • the image acquisition device is installed in the classroom in the embodiment of the present application, and the image collection area of the image collection device includes a podium area of the classroom.
  • the image collected by the image collection device includes a face image
  • at least one picture is acquired. It is guaranteed that the face image of the teacher can be collected to ensure the correctness of the subsequent attendance judgment.
  • the image of the teacher in the classroom during the class is in the database.
  • the similarity degree returns the similarity information and the teacher information, and the electronic device judges whether the attendance of the teacher in the classroom is normal according to the classroom information of the classroom, thereby further ensuring the accuracy of the attendance monitoring of the teacher, due to the initiative through the electronic device. Identification, no need for manual identification by experienced staff, thus effectively improving the efficiency of attendance monitoring and simplifying attendance monitoring operations.
  • an image collecting device may be installed in each classroom, and the identification information of each image capturing device is stored in the electronic device, and the classroom in which the image capturing device of each identification information is located is recorded, that is, The correspondence between the identification information of the image collection device and the classroom to which it is installed is stored in the electronic device.
  • the electronic device also acquires course information, which records which teacher in each time zone corresponding to each class has a class.
  • course information which records which teacher in each time zone corresponding to each class has a class.
  • the course information changes for some reason, for example, if one course in one classroom is adjusted to another, or the teacher A in a class changes to teacher B, the changes in the course information need to be synchronized in time.
  • the electronic device stores a face comparison database, and a large number of pictures containing the teacher face image are stored in the face comparison database, and it can be considered that all face data is stored in the face comparison database.
  • the information of the teacher corresponding to the picture is recorded according to the teacher face image included in the picture.
  • the teacher's information may be the name of the teacher, or the teacher's job number, number, etc., which uniquely determines the teacher's information.
  • the image of the face to be recognized in the picture is similar to the image in the face comparison database for each picture.
  • Degree when determining the attendance result of the teacher corresponding to the classroom in the time period, the image of the teacher in the classroom in which the class is scheduled in the face comparison database may be obtained; and then the person to be identified in the picture may be determined for each picture Whether the similarity between the face image and the acquired image is greater than a predetermined similarity threshold, and if so, increasing the number of recorded pictures by one; finally determining whether the number of recorded pictures is greater than a set number threshold, and if so, determining During this time period, the attendance of the teacher corresponding to the classroom is normal, otherwise, the attendance abnormality of the teacher corresponding to the classroom is determined during the time period.
  • the face image to be recognized in the at least one picture acquired at the current time is an image of the teacher in the classroom during the class. Then determine that the teacher's attendance is normal at that time.
  • the electronic device when it acquires 4 pictures, it can correspondingly set the data threshold to 2.
  • the number of recorded pictures is increased by one, and after each picture is compared, It can be determined whether the number of recorded pictures is greater than a quantity threshold, and if so, it is determined that the attendance of the teacher corresponding to the classroom is normal during the time period.
  • the electronic device determines the classroom according to the similarity between the image of the face to be recognized in the picture and the image in the face comparison database.
  • an image of the teacher in which the classroom is arranged in the class in the face comparison database may be acquired; then, the similarity between the face image to be recognized in each picture and the acquired image is determined, and the Determining the maximum value of the similarity; finally determining whether the maximum value of the similarity is greater than a predetermined similarity threshold, and if so, determining the teacher corresponding to the classroom in the time period
  • the attendance is normal, otherwise, the attendance abnormality of the teacher corresponding to the classroom is determined during the time period.
  • the face image to be recognized in the at least one picture acquired at the current time is an image of the teacher in the classroom during the class. Then determine that the teacher's attendance is normal at that time.
  • the electronic device when acquiring an image of the teacher in which the classroom is scheduled during the class, the electronic device may first retrieve the pre-stored course information of the classroom, wherein each course information is recorded in the course information.
  • the information of the teacher corresponding to the picture is recorded according to the teacher face image included in the picture.
  • the teacher's information may be the name of the teacher, or the teacher's job number, number, etc., which uniquely determines the teacher's information. Therefore, after knowing the name or number of the teacher in the classroom at the time of the class, an image of the corresponding teacher can be obtained in the face comparison database, that is, an image of the teacher who attends the class during the class.
  • the similarity value of the face image to be recognized in the image collected by the image capturing device and the acquired image may be The difference is large.
  • the similarity between the teacher's picture collected by the image acquisition device and the teacher's image in the face comparison database may be relatively large; for some teachers, the image collected by the image collection device is compared with the face of the teacher.
  • the similarity of the teacher's images in the database may be relatively small.
  • the electronic device may adjust the similarity threshold corresponding to the teacher according to whether the attendance result of each teacher is corrected.
  • the picture and the person of the teacher collected by the image collecting device are indicated.
  • the similarity of the teacher's image in the face comparison database is relatively low, which In this case, the similarity threshold can be lowered.
  • the attendance of the teacher corresponding to the classroom is normal, and the attendance result of the teacher corresponding to the classroom in the time period or the class is reversed, or the face image to be recognized in each picture and the acquisition
  • the similarity of the image is greater than the similarity threshold, it indicates that the similarity between the image of the teacher collected by the image collecting device and the image of the teacher in the face comparison database is relatively high. In this case, the height can be increased. Similarity threshold.
  • the lowering the similarity threshold may include: lowering the similarity threshold according to the set first proportional coefficient, such as 2%, 3%, 5%, etc.; or determining whether the reduced similarity threshold is low.
  • a preset minimum threshold such as 60%, 55%, 50%, etc., if yes, adjusting the similarity threshold to the lowest threshold; or identifying a face image to be recognized in each of the pictures and the class hour
  • Increasing the similarity threshold may include: increasing the similarity threshold according to the set second proportional coefficient, such as 2%, 4%, 5%, etc.; or determining whether the similarity threshold after the adjustment is greater than a preset maximum a threshold, such as 80%, 85%, 90%, etc., if yes, adjusting the similarity threshold to the highest threshold; or identifying a face image to be recognized in each of the pictures and the classroom is scheduled for the class
  • determining, according to the attendance result of the teacher corresponding to the classroom in each time period, determining the attendance result of the teacher corresponding to the classroom in the class time may include: determining whether there is a time period of the attendance abnormality; If it does not exist, it is determined that the attendance of the teacher corresponding to the classroom in the class is normal; if yes, the type of the attendance abnormality of the teacher corresponding to the classroom in the class is determined according to the time period corresponding to the attendance abnormality.
  • the determining, according to the time period corresponding to the attendance abnormality, the type of the attendance abnormality of the teacher corresponding to the classroom in the class time includes: when the time period corresponding to the attendance abnormality is the first time period in the class, determining the attendance abnormality The type is not on time; when the time period corresponding to the attendance abnormality is the last time period in the class, the type of the attendance abnormality is determined to be the class in advance; the time corresponding to the attendance abnormality is neither the first in the class. When the time period is not the last time period, it is determined that the type of attendance abnormality is left midway.
  • the electronic device may repeat the execution once or The following steps are performed multiple times, and it is determined whether the number of abnormal attendance results in the time period is greater than a preset threshold, such as 3, 4, 5, etc., if yes, the attendance abnormality of the teacher corresponding to the classroom is finally determined:
  • a preset threshold such as 3, 4, 5, etc.
  • the attendance abnormality of the teacher corresponding to the classroom is finally determined:
  • the image acquired by the image collection device is acquired again, and it is determined whether the image includes a face image; if yes, at least one image is acquired by the image collection device; and for each image, according to the image
  • the similarity between the face image to be recognized and the image in the face comparison database determines whether the attendance of the teacher corresponding to the classroom is normal during the time period.
  • the image capturing device may be a trackball.
  • the electronic device can determine whether the state of the trackball is a stationary state, and if so, acquire the captured image.
  • FIG. 2 is a schematic structural diagram of a teacher attendance attendance monitoring apparatus according to an embodiment of the present application.
  • An image capturing device is installed in a classroom, and an image capturing area of the image capturing device includes a platform area of a classroom, and any class corresponding to the classroom Divided into a plurality of time periods, the device includes:
  • the obtaining module 210 is configured to acquire an image of the podium area collected by the image collection device installed in the classroom during at least one time period of the class;
  • the processing module 220 is configured to determine whether the image is included in the image; if yes, obtain at least one picture by using the image collecting device; and for each picture, according to the face image and the face to be recognized in the image Comparing the similarity of the images in the database, determining the attendance result of the teacher corresponding to the classroom in the time period;
  • the determining module 230 is configured to determine the attendance result of the teacher corresponding to the classroom in the class according to the attendance result of the teacher corresponding to the classroom in each time period.
  • the teacher attendance monitoring device has an image capturing device installed in the classroom, and the image capturing area of the image capturing device includes a podium area of the classroom, and the face image is included in the image collected by the image collecting device. , get at least one picture, be sure to It is enough to collect the face image of the teacher to ensure the correctness of the subsequent attendance judgment.
  • the face image included in the at least one picture is similar to the image of the teacher in the class in the face comparison database in the class.
  • Degree judging whether the attendance of the teacher in the classroom during the class is normal, so that the accuracy of the attendance monitoring of the teacher can be further ensured, because the active identification by the electronic device does not require manual identification by an experienced staff member, thereby effectively improving the attendance monitoring. Efficiency and streamlined attendance monitoring operations.
  • the processing module 220 includes:
  • a processing submodule for determining, for each picture, whether the similarity between the face image to be recognized in the picture and the acquired image is greater than a predetermined similarity threshold, and if so, the recorded The number of pictures is increased by 1;
  • a first determining sub-module (not shown), configured to determine whether the number of recorded pictures is greater than a set number threshold, and if yes, determining that the attendance of the teacher corresponding to the classroom is normal during the time period; otherwise, determining the The attendance of the teacher corresponding to the classroom is abnormal during the time period.
  • the processing module 220 includes:
  • Identifying a sub-module (not shown) for determining a similarity between the face image to be recognized in each picture and the acquired image, and identifying a maximum value of the similarity
  • a second determining sub-module (not shown in the figure), configured to determine whether a maximum value of the similarity is greater than a predetermined similarity threshold, and if yes, determining that the attendance of the teacher corresponding to the classroom is normal during the time period, otherwise, determining The attendance of the teacher corresponding to the classroom during this time period is abnormal.
  • the acquiring sub-module is specifically configured to retrieve pre-stored course information of the classroom, where the course information records the name or number of the teacher who is scheduled for each class. According to the name or number of the class teacher who gave the class during the class, Obtain the corresponding image of the class teacher in the face comparison database.
  • the device further includes:
  • a lowering module (not shown) for determining an attendance abnormality of the teacher corresponding to the classroom during the time period or the class, and the attendance result of the teacher corresponding to the classroom is corrected during the time period or the class hour Low the similarity threshold;
  • the height adjustment module (not shown) is configured to determine that the attendance of the teacher corresponding to the classroom is normal when the time period or the class is determined, and the attendance result of the teacher corresponding to the classroom in the time period or the class is reversed, or And, when the similarity between the face image to be recognized and the acquired image in each picture is greater than the similarity threshold, the similarity threshold is raised.
  • the lowering module is specifically configured to lower the similarity threshold according to the set first proportional coefficient; and determine whether the reduced similarity threshold is lower than a preset minimum a threshold, if yes, adjusting the similarity threshold to the lowest threshold; or
  • the height adjustment module is specifically configured to increase the similarity threshold according to the set second proportional coefficient
  • the determining module 230 includes:
  • a judging sub-module (not shown) for determining whether there is a time period for attendance abnormality
  • Determining a sub-module for determining that the attendance of the teacher corresponding to the classroom in the class is normal when the judgment sub-module determines that the result is no; when the judgment sub-module determines that the result is yes, The type of the attendance abnormality of the teacher corresponding to the classroom in the class is determined according to the time period corresponding to the attendance abnormality.
  • the determining sub-module is specifically configured to: when the time period corresponding to the attendance abnormality is the first time period in the class time, determine that the type of the attendance abnormality is not on time; When the time period corresponding to the attendance abnormality is the last time period in the lesson, it is determined that the type of the attendance abnormality is the early class; the time corresponding to the attendance abnormality is neither the first time period nor the last time in the class time. For a period of time, it is determined that the type of attendance exception is left midway.
  • the device further includes:
  • An execution module (not shown) is configured to repeatedly perform one or more of the following steps when determining the attendance abnormality of the teacher corresponding to the classroom in the first round for any period of time, and determining the time period
  • the attendance result is whether the number of abnormal times is greater than a preset threshold, and if so, the attendance abnormality of the teacher corresponding to the classroom in the time period is finally determined:
  • the image acquired by the image collection device is acquired again, and it is determined whether the image includes a face image; if yes, at least one image is acquired by the image collection device; and for each image, according to the image
  • the similarity between the face image to be recognized and the image in the face comparison database determines whether the attendance of the teacher corresponding to the classroom is normal during the time period.
  • the processing module is further configured to determine, at the beginning of each time period, whether the state of the trackball is a static state, if Yes, the acquired image is acquired.
  • FIG. 3 is a schematic structural diagram of a teacher attendance monitoring system according to an embodiment of the present application.
  • the system includes an image capturing device 310 installed in a classroom, and a teacher attendance monitoring device shown in FIG. 2 located in the electronic device 320.
  • the teacher attendance attendance monitoring system since the image capturing device is installed in the classroom, and the image capturing area of the image capturing device includes the podium area of the classroom, when the image collected by the image collecting device includes the face image Obtain at least one picture, ensure that the face image of the teacher can be collected, and ensure the correctness of the subsequent attendance judgment. In the specific judgment, according to the face image and the face included in the at least one picture, the class time in the database is compared.
  • the present application further provides a storage medium, wherein the storage medium is used to store executable program code, and the executable program code is used to execute a teacher attendance monitoring method according to the present application at runtime.
  • the method for monitoring the attendance of the teacher in the application the image collection device is installed in the classroom, and the image collection area of the image collection device includes a podium area of the classroom, and any class corresponding to the classroom is divided into multiple For a period of time, the method includes:
  • Determining whether the image is included in the image if yes, acquiring at least one picture by the image collecting device; and for each picture, according to the image of the face to be recognized in the picture and the image in the face comparison database Similarity, determining the attendance result of the teacher corresponding to the classroom in the time period;
  • the attendance result of the teacher corresponding to the classroom in each time period is determined.
  • the image acquisition device is installed in the classroom in the embodiment of the present application, and the image collection area of the image collection device includes a podium area of the classroom.
  • the image collected by the image collection device includes a face image
  • at least one picture is acquired. It is guaranteed that the face image of the teacher can be collected to ensure the correctness of the subsequent attendance judgment.
  • the image of the teacher in the classroom during the class is in the database.
  • the similarity degree is used to judge whether the attendance of the teacher in the classroom is normal during the class, so that the accuracy of the attendance monitoring of the teacher can be further ensured, and the attendance is effectively recognized by the electronic device, and the manual identification is not required by the experienced staff, thereby effectively improving the attendance. Monitors efficiency and simplifies attendance monitoring operations.
  • the present application further provides an application, wherein the application is used to execute a teacher attendance monitoring method as described in the present application at runtime.
  • the application described An attendance monitoring method for a teacher wherein an image capturing device is installed in the classroom, and an image capturing area of the image capturing device includes a podium area of the classroom, and any class corresponding to the classroom is divided into a plurality of time periods, the method include:
  • Determining whether the image is included in the image if yes, acquiring at least one picture by the image collecting device; and for each picture, according to the image of the face to be recognized in the picture and the image in the face comparison database Similarity, determining the attendance result of the teacher corresponding to the classroom in the time period;
  • the attendance result of the teacher corresponding to the classroom in each time period is determined.
  • the image acquisition device is installed in the classroom in the embodiment of the present application, and the image collection area of the image collection device includes a podium area of the classroom.
  • the image collected by the image collection device includes a face image
  • at least one picture is acquired. It is guaranteed that the face image of the teacher can be collected to ensure the correctness of the subsequent attendance judgment.
  • the image of the teacher in the classroom during the class is in the database.
  • the similarity degree is used to judge whether the attendance of the teacher in the classroom is normal during the class, so that the accuracy of the attendance monitoring of the teacher can be further ensured, and the attendance is effectively recognized by the electronic device, and the manual identification is not required by the experienced staff, thereby effectively improving the attendance. Monitors efficiency and simplifies attendance monitoring operations.
  • an electronic device including:
  • processor a memory, a communication interface, and a bus
  • the processor, the memory, and the communication interface are connected by the bus and complete communication with each other;
  • the memory stores executable program code
  • the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory for performing a teacher attendance monitoring method as described in the present application.
  • a teacher attendance monitoring method described in the present application installed in the classroom
  • the image capturing area of the image capturing device includes a podium area of the classroom, and any class corresponding to the class is divided into a plurality of time periods, and the method includes:
  • Determining whether the image is included in the image if yes, acquiring at least one picture by the image collecting device; and for each picture, according to the image of the face to be recognized in the picture and the image in the face comparison database Similarity, determining the attendance result of the teacher corresponding to the classroom in the time period;
  • the attendance result of the teacher corresponding to the classroom in each time period is determined.
  • the image acquisition device is installed in the classroom in the embodiment of the present application, and the image collection area of the image collection device includes a podium area of the classroom.
  • the image collected by the image collection device includes a face image
  • at least one picture is acquired. It is guaranteed that the face image of the teacher can be collected to ensure the correctness of the subsequent attendance judgment.
  • the image of the teacher in the classroom during the class is in the database.
  • the similarity degree is used to judge whether the attendance of the teacher in the classroom is normal during the class, so that the accuracy of the attendance monitoring of the teacher can be further ensured, and the attendance is effectively recognized by the electronic device, and the manual identification is not required by the experienced staff, thereby effectively improving the attendance. Monitors efficiency and simplifies attendance monitoring operations.

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Abstract

本申请实施例公开了一种教师上课考勤监控方法、系统及装置，用以简化教师考勤监控的流程，并保证获取教师考勤的准确性及高效性。该方法包括：在任一课时的至少一个时间段内，获取教室中安装的图像采集设备采集的所述讲台区域的图像；判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。

Description

一种教师上课考勤监控方法、系统及装置
本申请要求于2016年6月12日提交中国专利局、申请号为201610409161.5发明名称为“一种教师上课考勤监控方法、系统及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及图像处理技术领域，特别涉及一种教师上课考勤监控方法、系统及装置。
背景技术
考勤系统是企业、学校等单位的人力资源管理的核心工具。对于企业来讲，考勤系统的应用往往和人员绩效和薪资挂钩，因为受到各级管理者的高度重视，而对于学校来讲，考勤数据对学生和教师都非常的重要，是对学生和教师评价的重要依据。
现有技术中，在对教师进行考勤监控时，一般在每个教室安装图像采集设备，通过图像采集设备获取教室内的图像。监控人员如果想对A教师的考勤进行监控，则需要确定该教师当前在哪个教室上课，从而调出该教室的图像，根据图像中是否存在教师，当存在教师时，还需要判断该教师是否为A教师，才能对教师上课考勤进行监控。
通过监控人员手动操作并确定教师考勤的方式操作较繁琐；另外，由于视频盲角以及图像采集的偶然性的问题，调出的图像中可能没有教师的画面，从而导致判断错误；或者因为图像采集设备的清晰度不够，只有放大才能分辨是否是正确的教师在上课。同时上述监控方案中还需要监控人员认识每个教师，才能判断出是否是正确的教师在上课，并且需要对每个教师相关课程信息都比较了解，可操作性较差，并且对监控人员的要求也比较高，无法有效的兼顾监控的准确性和监控效率。
发明内容
本申请实施例公开了一种教师上课考勤监控方法、系统及装置，用以简 化教师考勤监控的流程，并保证获取教师考勤的准确性及高效性。
第一方面，本申请实施例公开了一种教师上课考勤监控方法，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述方法包括：
在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
可选地，所述针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果包括：
获取人脸对比数据库中该课时该教室被排课的教师的图像；
针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度是否大于预定的相似度阈值，如果是，则将记录的图片的数量加1；
判断记录的图片的数量是否大于设定的数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
可选地，所述针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果包括：
获取人脸对比数据库中该课时该教室被排课的教师的图像；
确定每张图片中待识别的人脸图像与获取的图像的相似度，并识别所述相似度的最大值；
判断所述相似度的最大值是否大于预定的相似度阈值，如果是，确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
可选地，所述获取人脸对比数据库中该课时该教室被排课的教师的图像包括：
调取预先保存的该教室的课程信息，其中该课程信息中记录有每个课时被排课的教师的名字或编号；
根据调取的所述该教室该课时的排课教师的名字或编号，在人脸对比数据库中获取对应的该排课教师的图像。
可选地，所述方法还包括：
当确定该时间段或该课时该教室对应的教师的考勤异常，且该时间段或该课时该教室对应的教师的考勤结果被矫正时，调低所述相似度阈值；
当确定该时间段或该课时该教室对应的教师的考勤正常，且该时间段或该课时该教室对应的教师的考勤结果被矫反，或者，每张图片中待识别的人脸图像与获取的图像的相似度均大于所述相似度阈值时，调高所述相似度阈值。
可选地，所述调低所述相似度阈值包括：
按照设定的第一比例系数调低所述相似度阈值；
判断调低后的相似度阈值是否低于预设最低阈值，如果是，则将所述相似度阈值调整为所述最低阈值；或
识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否低于预设最低阈值，如果否，则将所述相似度阈值调整为所述最小值；
所述调高所述相似度阈值包括：
按照设定的第二比例系数调高所述相似度阈值；
判断调高后的相似度阈值是否大于预设最高阈值，如果是，则将所述相 似度阈值调整为所述最高阈值；或
识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否高于预设最高阈值，如果是，则将所述相似度阈值调整为所述最小值。
可选地，所述根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果包括：
判断是否存在考勤异常的时间段；
如果不存在，则确定该课时内该教室对应的教师的考勤正常；如果存在，则根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型。
可选地，所述根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型包括：
当考勤异常对应的时间段为该课时内的第一个时间段时，则确定考勤异常的类型为未按时上课；
当考勤异常对应的时间段为该课时内的最后一个时间段时，则确定考勤异常的类型为提前下课；
当考勤异常对应的时间段既不是该课时内的第一个时间段，也不是最后一个时间段时，则确定考勤异常的类型为中途离开。
可选地，当针对任一时间段，首轮确定该时间段该教室对应的教师的考勤异常时，所述方法还包括：重复执行一次或多次以下步骤，并判断该时间段内考勤结果为异常的次数是否大于预设阈值，如果是，则最终确定该时间段该教室对应的教师的考勤异常：
在该时间段内，再次获取所述图像采集设备采集的图像，判断该图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤是否正常。
可选地，当所述图像采集设备为跟踪球时，所述方法还包括：
在每个时间段开始时，判断所述跟踪球的状态是否为静止状态，如果是，则获取采集的图像。
第二方面，本申请实施例公开了一种教师上课考勤监控装置，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述装置包括：
获取模块，用于在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
处理模块，用于判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
确定模块，用于根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
可选地，所述处理模块包括：
获取子模块，用于获取人脸对比数据库中该课时该教室被排课的教师的图像；
处理子模块，用于针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度是否大于预定的相似度阈值，如果是，则将记录的图片的数量加1；
第一确定子模块，用于判断记录的图片的数量是否大于设定的数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
可选地，所述处理模块包括：
获取子模块，用于获取人脸对比数据库中该课时该教室被排课的教师的图像；
识别子模块，用于确定每张图片中待识别的人脸图像与获取的图像的相似度，并识别所述相似度的最大值；
第二确定子模块，用于判断所述相似度的最大值是否大于预定的相似度阈值，如果是，确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
可选地，所述获取子模块，具体用于调取预先保存的该教室的课程信息，其中该课程信息中记录有每个课时被排课的教师的名字或编号；根据调取的所述该教室该课时的排课教师的名字或编号，在人脸对比数据库中获取对应的该排课教师的图像。
可选地，所述装置还包括：
调低模块，用于当确定该时间段或该课时该教室对应的教师的考勤异常，且该时间段或该课时该教室对应的教师的考勤结果被矫正时，调低所述相似度阈值；
调高模块，用于当确定该时间段或该课时该教室对应的教师的考勤正常，且该时间段或该课时该教室对应的教师的考勤结果被矫反，或者，每张图片中待识别的人脸图像与获取的图像的相似度均大于所述相似度阈值时，调高所述相似度阈值。
可选地，所述调低模块，具体用于按照设定的第一比例系数调低所述相似度阈值；判断调低后的相似度阈值是否低于预设最低阈值，如果是，则将所述相似度阈值调整为所述最低阈值；或
识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否低于预设最低阈值，如果否，则将所述相似度阈值调整为所述最小值；
所述调高模块，具体用于按照设定的第二比例系数调高所述相似度阈值；
判断调高后的相似度阈值是否大于预设最高阈值，如果是，则将所述相似度阈值调整为所述最高阈值；或
识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否高于预设最高阈值，如果是，则将所述相似度阈值调整为所述最小值。
可选地，所述确定模块，包括：
判断子模块，用于判断是否存在考勤异常的时间段；
确定子模块，用于当所述判断子模块判断结果为否时，确定该课时内该教室对应的教师的考勤正常；当所述判断子模块判断结果为是时，根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型。
可选地，所述确定子模块，具体用于当考勤异常对应的时间段为该课时内的第一个时间段时，则确定考勤异常的类型为未按时上课；当考勤异常对应的时间段为该课时内的最后一个时间段时，则确定考勤异常的类型为提前下课；当考勤异常对应的时间段既不是该课时内的第一个时间段，也不是最后一个时间段时，则确定考勤异常的类型为中途离开。
可选地，所述装置还包括：
执行模块，用于当针对任一时间段，首轮确定该时间段该教室对应的教师的考勤异常时，重复执行一次或多次以下步骤，并判断该时间段内考勤结果为异常的次数是否大于预设阈值，如果是，则最终确定该时间段该教室对应的教师的考勤异常：
在该时间段内，再次获取所述图像采集设备采集的图像，判断该图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤是否正常。
可选地，当所述图像采集设备为跟踪球时，所述处理模块还用于在每个时间段开始时，判断所述跟踪球的状态是否为静止状态，如果是，则获取采集的图像。
第三方面，本申请实施例公开了一种教师上课考勤监控系统，所述系统包括安装在教室的图像采集设备，及如上述第二方面所述的教师上课考勤监控装置。
第四方面，本申请提供了一种存储介质，其中，该存储介质用于存储可执行程序代码，所述可执行程序代码用于在运行时执行如上述第一 方面所述的一种教师上课考勤监控方法。
第五方面，本申请提供了一种应用程序，其中，该应用程序用于在运行时执行如上述第一方面所述的一种教师上课考勤监控方法。
第六方面，本申请提供了一种电子设备，包括：
处理器、存储器、通信接口和总线；
所述处理器、所述存储器和所述通信接口通过所述总线连接并完成相互间的通信；
所述存储器存储可执行程序代码；
所述处理器通过读取所述存储器中存储的可执行程序代码来运行与所述可执行程序代码对应的程序，以用于执行如上述第一方面所述的一种教师上课考勤监控方法。
本申请实施例提供了一种教师上课考勤监控方法、系统及装置，教室中安装有图像采集设备，该图像采集设备的图像采集区域包括教室的讲台区域，该教室对应的任一课时被划分为多个时间段，该方法包括：在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。由于本申请实施例中在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，判断该课时该教室上课的教师的考勤是否正常，从而可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
附图说明
为了更清楚地说明本申请实施例和现有技术的技术方案，下面对实施例和现有技术中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的一种教师上课考勤监控方法流程图；
图2为本申请实施例提供的一种教师上课考勤监控装置结构示意图；
图3为本申请实施例提供的一种教师上课考勤监控系统结构示意图。
具体实施方式
为了简化教师考勤监控操作，提高教师考勤监控的效率及准确性，本申请实施例提供了一种教师上课考勤监控方法、系统及装置。
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
图1为本申请实施例提供的一种教师上课考勤监控方法流程图，该方法可以包括：
S101：在该课时的至少一个时间段内，获取教室中安装的图像采集设备采集的所述讲台区域的图像。
本申请实施例可以应用于电子设备，该电子设备可以是任意具有识别监控功能的设备，例如可以为服务器等。
为了实现对教师的考勤进行监控，在本申请实施例中，在教室中安装有图像采集设备，该图像采集设备的图像采集区域包括教室的讲台区域。并且，该图像采集设备与电子设备之间通过有线或无线方式连接。
需要说明的是，在本申请实施例中，可以以电子设备监控任一教室，任一课时为例，来说明本申请实施例的教师上课考勤监控方法。在实际应用中， 可以采用本申请实施例提供的方法，通过电子设备来监控每个教室每个课时的教师考勤情况。
因为对于学校而言，其上课时间、下课时间是固定的，因此每个课时对应的时间段也是确定的。为了准确的确定教师在任一课时内的考勤，在本申请实施例中将该课时划分为了多个时间段，例如可以划分为3个、4个或者5个等时间段。通过对该课时的每个时间段内该教室的考勤进行监控，确定该课时内该教室对应的教师的考勤结果。
在采用本申请提供的方法监控每个课时的教师考勤情况时，每个课时划分的时间段的数量可以相同也可以不同，每个时间段的长度可以相同，也可以不同。较佳地，为了减少划分的工作量，可以将每个课时划分为相同的多个时间段，例如将每个课时等分为4个时间段。
在本申请实施例中，在该课时的至少一个时间段内，电子设备可以获取教室中安装的图像采集设备采集的讲台区域的图像。
S102：判断所述图像中是否包含人脸图像；如果是，进行步骤S103，如果否，则返回执行步骤S101中获取所述教室中安装的图像采集设备采集的所述讲台区域的图像的步骤。
其中，在采集的图像中判断是否包含人脸图像的过程可以采用现有技术，本申请实施例对此过程不进行赘述。
S103：通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果。
人脸对比数据库存储在电子设备本地，人脸对比数据库中保存有每个教师的人脸图片。在本申请实施例中为了进一步保证对教师考勤监控的准确性，在每个时刻，通过图像采集设备获取了至少一张图片，例如可以是3张、5张图片等，确定每张图片中待识别的人脸图像与人脸对比数据库中对应教师的图像的相似度，如果某一张图片中待识别的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度大于设定的相似度阈值，则确定该图片中记录的教师考勤正常。如果该时刻获取的至少一张图片中，记录教师考 勤正常的图片的数量大于设定的数量阈值时，则确定该图片中记录的教师考勤正常。
S104：根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
一个课时包括多个时间段，在确定该课时该教室上课的教师的考勤结果时，可以根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
例如，可以判断考勤正常的时间段的数量是否大于预定阈值，如果是，可以确定该课时内该教室对应的教师的考勤正常；否则，确定该课时内该教室对应的教师的考勤异常。
由于本申请实施例中在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，返回相似度信息及教师信息，电子设备根据该教室的课程信息判断该课时该教室上课的教师的考勤是否正常，从而可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
为了实现对教师的考勤的监控，可以在每个教室安装图像采集设备，在电子设备中保存有每个图像采集设备的标识信息，并记录有每个标识信息的图像采集设备所在的教室，即在电子设备中保存有图像采集设备的标识信息与其所安装的教室的对应关系。
另外，电子设备还获取了课程信息，该课程信息中记录有每个教室哪个课时对应的时间范围中哪个教师上课。当然如果因为某些原因出现了课程信息的更改，例如某一教室的某个课程调整到了另一教室，或者某个课时上课的教师A更换为了教师B，这些课程信息的更改也需要及时的同步到电子设 备中，以保证对相应教师考勤监控的准确性。
电子设备中存储有人脸对比数据库，在该人脸对比数据库中保存有大量的包含有教师人脸图像的图片，可以认为人脸对比数据库中保存有所有的人脸数据。为了实现对教师考勤的监控，在本申请实施例中针对人脸对比数据库中的保存的每张图片，根据该图片中包含的教师人脸图像，记录有该图片对应的教师的信息。该教师的信息可以是教师的名称，也可以是教师的工号、编号等唯一确定该教师的信息。
在本申请实施例的一种可选实施方式中，当电子设备获取了多张图片时，其针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果时，可以获取人脸对比数据库中该课时该教室被排课的教师的图像；然后可以针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度是否大于预定的相似度阈值，如果是，则将记录的图片的数量加1；最后判断记录的图片的数量是否大于设定的数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
在本申请的上述实施例中，当记录的图片的数量大于设定的数量阈值时，说明当前时刻获取的至少一张图片中待识别的人脸图像为该课时该教室上课的教师的图像，则确定该时刻教师的考勤正常。
例如，当电子设备获取4张图片时，其可以相应的，将数据阈值设置为2。当针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度大于预定的相似度阈值时，将记录的图片的数量加1，当每张图片均比对完成后，可以判断记录的图片的数量是否大于数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常。
在本申请实施例的另一种可选实施方式中，针对每张图片，电子设备根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果时，可以获取人脸对比数据库中该课时该教室被排课的教师的图像；然后确定每张图片中待识别的人脸图像与获取的图像的相似度，并识别所述相似度的最大值；最后判断所述相似度的最大值是否大于预定的相似度阈值，如果是，确定该时间段该教室对应的教师的 考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
在本申请的上述实施例中，当相似度的最大值大于预定的相似度阈值时，说明当前时刻获取的至少一张图片中待识别的人脸图像为该课时该教室上课的教师的图像，则确定该时刻教师的考勤正常。
其中，上述实施例中，电子设备在获取人脸对比数据库中该课时该教室被排课的教师的图像时，可以首先调取预先保存的该教室的课程信息，其中该课程信息中记录有每个课时被排课的教师的名字或编号；然后可以根据调取的该教室该课时的排课教师的名字或编号，在人脸对比数据库中获取对应的该排课教师的图像。
在本申请实施例中，针对人脸对比数据库中的保存的每张图片，根据该图片中包含的教师人脸图像，记录有该图片对应的教师的信息。该教师的信息可以是教师的名称，也可以是教师的工号、编号等唯一确定该教师的信息。因此，当得知该课时该教室上课的教师的名字或编号后，可以在人脸对比数据库中获取对应的教师的图像，即为该课时该教室上课的教师的图像。
可以理解，有些情况下，由于人脸对比数据库中保存的各教师的图像的清晰度不同，或其他原因，图像采集设备采集的图片中待识别的人脸图像与获取的图像的相似度值可能差别较大。如，针对部分教师，图像采集设备采集的该教师的图片与人脸对比数据库中该教师的图像的相似度可能均比较大；针对部分教师，图像采集设备采集的该教师的图片与人脸对比数据库中该教师的图像的相似度可能均比较小。
因此，当基于各教师的图片与人脸对比数据库中该教师的图像的相似度，确定教师的考勤结果时，可能出现考勤结果判定不准确的情况。
当出现考勤结果判定不准确时，可以由教务工作人员根据实际情况，对考勤结果进行矫正。这种情况下，为了提高后续的考勤结果的准确性，电子设备可以根据各教师的考勤结果是否被矫正，调整该教师对应的相似度阈值。
例如，当确定该时间段或该课时该教室对应的教师的考勤异常，且该时间段或该课时该教室对应的教师的考勤结果被矫正时，表明图像采集设备采集的该教师的图片与人脸对比数据库中该教师的图像的相似度均比较低，这 种情况下，可以调低该相似度阈值。
当确定该时间段或该课时该教室对应的教师的考勤正常，且该时间段或该课时该教室对应的教师的考勤结果被矫反，或者，每张图片中待识别的人脸图像与获取的图像的相似度均大于所述相似度阈值时，表明图像采集设备采集的该教师的图片与人脸对比数据库中该教师的图像的相似度均比较高，这种情况下，可以调高该相似度阈值。
具体地，调低该相似度阈值可以包括：按照设定的第一比例系数，如2％、3％、5％等，调低该相似度阈值；或判断调低后的相似度阈值是否低于预设最低阈值，如60％、55％、50％等，如果是，则将该相似度阈值调整为该最低阈值；或识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断该最小值是否低于预设最低阈值，如果否，则将该相似度阈值调整为该最小值。
调高该相似度阈值可以包括：按照设定的第二比例系数，如2％、4％、5％等，调高该相似度阈值；或判断调高后的相似度阈值是否大于预设最高阈值，如80％、85％、90％等，如果是，则将该相似度阈值调整为该最高阈值；或识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断该最小值是否高于预设最高阈值，如果是，则将该相似度阈值调整为该最小值。
作为本申请实施例的一种实施方式，根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果可以包括：判断是否存在考勤异常的时间段；如果不存在，则确定该课时内该教室对应的教师的考勤正常；如果存在，则根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型。
其中，根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型包括：当考勤异常对应的时间段为该课时内的第一个时间段时，则确定考勤异常的类型为未按时上课；当考勤异常对应的时间段为该课时内的最后一个时间段时，则确定考勤异常的类型为提前下课；当考勤异常对应的时间段既不是该课时内的第一个时间段，也不是最后一个时间段时，则确定考勤异常的类型为中途离开。
作为本申请实施例的一种实施方式，为了提高考勤结果的准确性，当针对任一时间段，首轮确定该时间段该教室对应的教师的考勤异常时，电子设备还可以重复执行一次或多次以下步骤，并判断该时间段内考勤结果为异常的次数是否大于预设阈值，如3、4、5等，如果是，则最终确定该时间段该教室对应的教师的考勤异常：在该时间段内，再次获取所述图像采集设备采集的图像，判断该图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤是否正常。
作为本申请实施例的一种实施方式，上述图像采集设备可以为跟踪球。并且，当图像采集设备为跟踪球时，为了保证采集的图像的清晰度，在每个时间段开始时，电子设备可以判断跟踪球的状态是否为静止状态，如果是，则获取采集的图像。
图2为本申请实施例提供的一种教师上课考勤监控装置结构示意图，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述装置包括：
获取模块210，用于在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
处理模块220，用于判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
确定模块230，用于根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
本申请实施例提供的教师上课考勤监控装置，由于在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能 够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，判断该课时该教室上课的教师的考勤是否正常，从而可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
作为本申请实施例的一种实施方式，所述处理模块220包括：
获取子模块(图中未示出)，用于获取人脸对比数据库中该课时该教室被排课的教师的图像；
处理子模块(图中未示出)，用于针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度是否大于预定的相似度阈值，如果是，则将记录的图片的数量加1；
第一确定子模块(图中未示出)，用于判断记录的图片的数量是否大于设定的数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
作为本申请实施例的一种实施方式，所述处理模块220包括：
获取子模块(图中未示出)，用于获取人脸对比数据库中该课时该教室被排课的教师的图像；
识别子模块(图中未示出)，用于确定每张图片中待识别的人脸图像与获取的图像的相似度，并识别所述相似度的最大值；
第二确定子模块(图中未示出)，用于判断所述相似度的最大值是否大于预定的相似度阈值，如果是，确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
作为本申请实施例的一种实施方式，所述获取子模块，具体用于调取预先保存的该教室的课程信息，其中该课程信息中记录有每个课时被排课的教师的名字或编号；根据调取的所述该教室该课时的排课教师的名字或编号， 在人脸对比数据库中获取对应的该排课教师的图像。
作为本申请实施例的一种实施方式，所述装置还包括：
调低模块(图中未示出)，用于当确定该时间段或该课时该教室对应的教师的考勤异常，且该时间段或该课时该教室对应的教师的考勤结果被矫正时，调低所述相似度阈值；
调高模块(图中未示出)，用于当确定该时间段或该课时该教室对应的教师的考勤正常，且该时间段或该课时该教室对应的教师的考勤结果被矫反，或者，每张图片中待识别的人脸图像与获取的图像的相似度均大于所述相似度阈值时，调高所述相似度阈值。
作为本申请实施例的一种实施方式，所述调低模块，具体用于按照设定的第一比例系数调低所述相似度阈值；判断调低后的相似度阈值是否低于预设最低阈值，如果是，则将所述相似度阈值调整为所述最低阈值；或
识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否低于预设最低阈值，如果否，则将所述相似度阈值调整为所述最小值；
所述调高模块，具体用于按照设定的第二比例系数调高所述相似度阈值；
判断调高后的相似度阈值是否大于预设最高阈值，如果是，则将所述相似度阈值调整为所述最高阈值；或
识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否高于预设最高阈值，如果是，则将所述相似度阈值调整为所述最小值。
作为本申请实施例的一种实施方式，所述确定模块230，包括：
判断子模块(图中未示出)，用于判断是否存在考勤异常的时间段；
确定子模块(图中未示出)，用于当所述判断子模块判断结果为否时，确定该课时内该教室对应的教师的考勤正常；当所述判断子模块判断结果为是时，根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型。
作为本申请实施例的一种实施方式，所述确定子模块，具体用于当考勤异常对应的时间段为该课时内的第一个时间段时，则确定考勤异常的类型为未按时上课；当考勤异常对应的时间段为该课时内的最后一个时间段时，则确定考勤异常的类型为提前下课；当考勤异常对应的时间段既不是该课时内的第一个时间段，也不是最后一个时间段时，则确定考勤异常的类型为中途离开。
作为本申请实施例的一种实施方式，所述装置还包括：
执行模块(图中未示出)，用于当针对任一时间段，首轮确定该时间段该教室对应的教师的考勤异常时，重复执行一次或多次以下步骤，并判断该时间段内考勤结果为异常的次数是否大于预设阈值，如果是，则最终确定该时间段该教室对应的教师的考勤异常：
在该时间段内，再次获取所述图像采集设备采集的图像，判断该图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤是否正常。
作为本申请实施例的一种实施方式，当所述图像采集设备为跟踪球时，所述处理模块还用于在每个时间段开始时，判断所述跟踪球的状态是否为静止状态，如果是，则获取采集的图像。
图3为本申请实施例提供的一种教师上课考勤监控系统结构示意图，所述系统包括安装在教室的图像采集设备310，及位于电子设备320中的图2所示的教师上课考勤监控装置。
本申请实施例提供的教师上课考勤监控系统，由于在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，判断该课时该教室上课的教师的考勤是否正常，从而 可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
相应的，本申请还提供了一种存储介质，其中，该存储介质用于存储可执行程序代码，所述可执行程序代码用于在运行时执行本申请所述的一种教师上课考勤监控方法。其中，本申请所述的一种教师上课考勤监控方法，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述方法包括：
在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
由于本申请实施例中在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，判断该课时该教室上课的教师的考勤是否正常，从而可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
相应的，本申请还提供了一种应用程序，其中，该应用程序用于在运行时执行如本申请所述的一种教师上课考勤监控方法。其中，本申请所述的 一种教师上课考勤监控方法，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述方法包括：
在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
由于本申请实施例中在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，判断该课时该教室上课的教师的考勤是否正常，从而可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
相应的，本申请还提供了一种电子设备，包括：
处理器、存储器、通信接口和总线；
所述处理器、所述存储器和所述通信接口通过所述总线连接并完成相互间的通信；
所述存储器存储可执行程序代码；
所述处理器通过读取所述存储器中存储的可执行程序代码来运行与所述可执行程序代码对应的程序，以用于执行如本申请所述的一种教师上课考勤监控方法。其中，本申请所述的一种教师上课考勤监控方法，教室中安装 有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述方法包括：
在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
由于本申请实施例中在教室中安装了图像采集设备，并且该图像采集设备的图像采集区域包括教室的讲台区域，在图像采集设备采集的图像中包含人脸图像时，获取至少一张图片，保证一定能够采集到教师的人脸图像，保证后续考勤判断的正确性，在具体判断时根据该至少一张图片中包含的人脸图像与人脸对比数据库中该课时该教室上课的教师的图像的相似度，判断该课时该教室上课的教师的考勤是否正常，从而可以进一步保证对教师考勤监控的准确性，由于通过电子设备主动识别，无需有经验的工作人员人工识别，因此有效提高了考勤监控的效率，并简化了考勤监控操作。
对于装置/系统/存储介质/应用程序/电子设备实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素， 并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
本说明书中的各个实施例均采用相关的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于装置实施例而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
本领域普通技术人员可以理解实现上述方法实施方式中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，所述的程序可以存储于计算机可读取存储介质中，这里所称得的存储介质，如：ROM/RAM、磁碟、光盘等。
以上所述仅为本申请的较佳实施例而已，并非用于限定本申请的保护范围。凡在本申请的精神和原则之内所作的任何修改、等同替换、改进等，均包含在本申请的保护范围内。

Claims (24)

  1. 一种教师上课考勤监控方法，其特征在于，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述方法包括：
    在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
    判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
    根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
  2. 根据权利要求1所述的方法，其特征在于，所述针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果包括：
    获取人脸对比数据库中该课时该教室被排课的教师的图像；
    针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度是否大于预定的相似度阈值，如果是，则将记录的图片的数量加1；
    判断记录的图片的数量是否大于设定的数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
  3. 根据权利要求1所述的方法，其特征在于，所述针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果包括：
    获取人脸对比数据库中该课时该教室被排课的教师的图像；
    确定每张图片中待识别的人脸图像与获取的图像的相似度，并识别所述相似度的最大值；
    判断所述相似度的最大值是否大于预定的相似度阈值，如果是，确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
  4. 根据权利要求2或3任一项所述的方法，其特征在于，所述获取人脸对比数据库中该课时该教室被排课的教师的图像包括：
    调取预先保存的该教室的课程信息，其中该课程信息中记录有每个课时被排课的教师的名字或编号；
    根据调取的所述该教室该课时的排课教师的名字或编号，在人脸对比数据库中获取对应的该排课教师的图像。
  5. 根据权利要求2或3任一项所述的方法，其特征在于，所述方法还包括：
    当确定该时间段或该课时该教室对应的教师的考勤异常，且该时间段或该课时该教室对应的教师的考勤结果被矫正时，调低所述相似度阈值；
    当确定该时间段或该课时该教室对应的教师的考勤正常，且该时间段或该课时该教室对应的教师的考勤结果被矫反，或者，每张图片中待识别的人脸图像与获取的图像的相似度均大于所述相似度阈值时，调高所述相似度阈值。
  6. 根据权利要求5所述的方法，其特征在于，所述调低所述相似度阈值包括：
    按照设定的第一比例系数调低所述相似度阈值；
    判断调低后的相似度阈值是否低于预设最低阈值，如果是，则将所述相似度阈值调整为所述最低阈值；或
    识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否低于预设最低阈值，如果否，则将所述相似度阈值调整为所述最小值；
    所述调高所述相似度阈值包括：
    按照设定的第二比例系数调高所述相似度阈值；
    判断调高后的相似度阈值是否大于预设最高阈值，如果是，则将所述相似度阈值调整为所述最高阈值；或
    识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否高于预设最高阈值，如果是，则将所述相似度阈值调整为所述最小值。
  7. 根据权利要求2或3任一项所述的方法，其特征在于，所述根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果包括：
    判断是否存在考勤异常的时间段；
    如果不存在，则确定该课时内该教室对应的教师的考勤正常；如果存在，则根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型。
  8. 根据权利要求7所述的方法，其特征在于，所述根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型包括：
    当考勤异常对应的时间段为该课时内的第一个时间段时，则确定考勤异常的类型为未按时上课；
    当考勤异常对应的时间段为该课时内的最后一个时间段时，则确定考勤异常的类型为提前下课；
    当考勤异常对应的时间段既不是该课时内的第一个时间段，也不是最后一个时间段时，则确定考勤异常的类型为中途离开。
  9. 根据权利要求1所述的方法，其特征在于，当针对任一时间段，首轮确定该时间段该教室对应的教师的考勤异常时，所述方法还包括：重复执行一次或多次以下步骤，并判断该时间段内考勤结果为异常的次数是否大于预设阈值，如果是，则最终确定该时间段该教室对应的教师的考勤异常：
    在该时间段内，再次获取所述图像采集设备采集的图像，判断该图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的 相似度，确定该时间段该教室对应的教师的考勤是否正常。
  10. 根据权利要求1所述的方法，其特征在于，当所述图像采集设备为跟踪球时，所述方法还包括：
    在每个时间段开始时，判断所述跟踪球的状态是否为静止状态，如果是，则获取采集的图像。
  11. 一种教师上课考勤监控装置，其特征在于，教室中安装有图像采集设备，所述图像采集设备的图像采集区域包括教室的讲台区域，所述教室对应的任一课时被划分为多个时间段，所述装置包括：
    获取模块，用于在该课时的至少一个时间段内，获取所述教室中安装的图像采集设备采集的所述讲台区域的图像；
    处理模块，用于判断所述图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤结果；
    确定模块，用于根据每个时间段该教室对应的教师的考勤结果，确定该课时内该教室对应的教师的考勤结果。
  12. 根据权利要求11所述的装置，其特征在于，所述处理模块包括：
    获取子模块，用于获取人脸对比数据库中该课时该教室被排课的教师的图像；
    处理子模块，用于针对每张图片，判断该图片中待识别的人脸图像与获取的图像的相似度是否大于预定的相似度阈值，如果是，则将记录的图片的数量加1；
    第一确定子模块，用于判断记录的图片的数量是否大于设定的数量阈值，如果是，则确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
  13. 根据权利要求11所述的装置，其特征在于，所述处理模块包括：
    获取子模块，用于获取人脸对比数据库中该课时该教室被排课的教师的图像；
    识别子模块，用于确定每张图片中待识别的人脸图像与获取的图像的相似度，并识别所述相似度的最大值；
    第二确定子模块，用于判断所述相似度的最大值是否大于预定的相似度阈值，如果是，确定该时间段该教室对应的教师的考勤正常，否则，确定该时间段该教室对应的教师的考勤异常。
  14. 根据权利要求12或13任一项所述的装置，其特征在于，所述获取子模块，具体用于调取预先保存的该教室的课程信息，其中该课程信息中记录有每个课时被排课的教师的名字或编号；根据调取的所述该教室该课时的排课教师的名字或编号，在人脸对比数据库中获取对应的该排课教师的图像。
  15. 根据权利要求12或13任一项所述的装置，其特征在于，所述装置还包括：
    调低模块，用于当确定该时间段或该课时该教室对应的教师的考勤异常，且该时间段或该课时该教室对应的教师的考勤结果被矫正时，调低所述相似度阈值；
    调高模块，用于当确定该时间段或该课时该教室对应的教师的考勤正常，且该时间段或该课时该教室对应的教师的考勤结果被矫反，或者，每张图片中待识别的人脸图像与获取的图像的相似度均大于所述相似度阈值时，调高所述相似度阈值。
  16. 根据权利要求15所述的装置，其特征在于，所述调低模块，具体用于按照设定的第一比例系数调低所述相似度阈值；判断调低后的相似度阈值是否低于预设最低阈值，如果是，则将所述相似度阈值调整为所述最低阈值；或
    识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否低于预设最低阈值，如果否，则将所述相似度阈值调整为所述最小值；
    所述调高模块，具体用于按照设定的第二比例系数调高所述相似度阈值；
    判断调高后的相似度阈值是否大于预设最高阈值，如果是，则将所述相似度阈值调整为所述最高阈值；或
    识别每张所述图片中待识别的人脸图像与该课时该教室被排课的教师的图像的相似度的最小值；判断所述最小值是否高于预设最高阈值，如果是，则将所述相似度阈值调整为所述最小值。
  17. 根据权利要求12或13任一项所述的装置，其特征在于，所述确定模块，包括：
    判断子模块，用于判断是否存在考勤异常的时间段；
    确定子模块，用于当所述判断子模块判断结果为否时，确定该课时内该教室对应的教师的考勤正常；当所述判断子模块判断结果为是时，根据考勤异常对应的时间段，确定该课时内该教室对应的教师的考勤异常的类型。
  18. 根据权利要求17所述的装置，其特征在于，所述确定子模块，具体用于当考勤异常对应的时间段为该课时内的第一个时间段时，则确定考勤异常的类型为未按时上课；当考勤异常对应的时间段为该课时内的最后一个时间段时，则确定考勤异常的类型为提前下课；当考勤异常对应的时间段既不是该课时内的第一个时间段，也不是最后一个时间段时，则确定考勤异常的类型为中途离开。
  19. 根据权利要求11所述的装置，其特征在于，所述装置还包括：
    执行模块，用于当针对任一时间段，首轮确定该时间段该教室对应的教师的考勤异常时，重复执行一次或多次以下步骤，并判断该时间段内考勤结果为异常的次数是否大于预设阈值，如果是，则最终确定该时间段该教室对应的教师的考勤异常：
    在该时间段内，再次获取所述图像采集设备采集的图像，判断该图像中是否包含人脸图像；如果是，通过所述图像采集设备获取至少一张图片；针对每张图片，根据该图片中待识别的人脸图像与人脸对比数据库中的图像的相似度，确定该时间段该教室对应的教师的考勤是否正常。
  20. 根据权利要求11所述的装置，其特征在于，当所述图像采集设备为跟踪球时，所述处理模块还用于在每个时间段开始时，判断所述跟踪球的状态是否为静止状态，如果是，则获取采集的图像。
  21. 一种教师上课考勤监控系统，其特征在于，所述系统包括安装在教室的图像采集设备，及权利要求11-20任一项所述的教师上课考勤监控装置。
  22. 一种存储介质，其特征在于，所述存储介质用于存储可执行程序代码，所述可执行程序代码用于在运行时执行如权利要求1-10任一项所述的一种教师上课考勤监控方法。
  23. 一种应用程序，其特征在于，所述应用程序用于在运行时执行如权利要求1-10任一项所述的一种教师上课考勤监控方法。
  24. 一种电子设备，包括：
    处理器、存储器、通信接口和总线；
    所述处理器、所述存储器和所述通信接口通过所述总线连接并完成相互间的通信；
    所述存储器存储可执行程序代码；
    所述处理器通过读取所述存储器中存储的可执行程序代码来运行与所述可执行程序代码对应的程序，以用于执行如权利要求1-10任一项所述的一种教师上课考勤监控方法。
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