Campus attendance checking method and device based on image tracking, identification terminal and system

Abstract

The invention discloses a campus attendance checking method based on image tracking. The campus attendance checking method comprises the following steps: acquiring real-time video streams acquired andgenerated by a plurality of camera monitoring devices arranged on a predetermined tracking path, wherein the tracking path is a path through which a student needs to enter a classroom; extracting face images from the real-time video stream, performing face recognition and tracking on each face image to obtain student information and a face movement track corresponding to each face image, and determining the time when the corresponding student information enters a classroom according to the face image track; and automatically generating attendance data corresponding to the student informationaccording to the student information and the classroom entering time.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a method, device, identification terminal and system for tracking campus attendance based on images. Background technique [0002] With the continuous development of technologies such as artificial intelligence technology and image processing, face recognition has been widely used in various fields of social activities. For example: face recognition smart unlocking, face recognition smart payment, face recognition smart security check, etc. [0003] The use of face recognition for attendance has been widely used, but when the traditional face recognition is applied to campus attendance, there are the following problems: [0004] 1. The dynamic resolution of the image is low, and it is impossible to judge the difference between the static image of the person and the real person; [0005] 2. There is a possibility that students forget to identify and lead to abnormal attendance; ...

AI Technical Summary

Problems solved by technology

[0004] 1. The dynamic resolution of the image is low, and it is impossible to judge the difference between the static image of the person and the real person;

[0005] 2. There is a possibility that students forget to identify and lead to abnormal attendance;

[0006] 3. Face recognition takes a lot of time, and there may be a possibility that students need to queue up for attendance

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