The Practice of a Method of Self-Study Students Counting in Classrooms Based on Head Detection in Colleges and Universities
Published: 1 January 2022
Journal of Computer and Communications , Volume 10, pp 51-62; https://doi.org/10.4236/jcc.2022.104005
Abstract: In order to solve the problem that it is difficult for students to find self-study classrooms because of the limited classroom resources, combined with the current situation of informatization in colleges and universities, a feasible method of students counting in classrooms based on head detection is proposed. This method first collects the scene images in the classroom at regular intervals based on the existing examination monitoring system, and then uses the offline trained AdaBoost cascade detector to detect the head candidate region in the images. Then, the trained CNN-SVM model is used to further identify the head, and finally the identification results are processed and the number of students in the classrooms is counted. The test and practice show that the query system for the idle situation of self-study classrooms constructed by coordinating the classroom seat capacity, classroom scheduling data and the students counting in the classroom based on the above method can easily query the current crowded degree of the students in the classrooms, which plays a good guiding role for students to find self-study classrooms. The method has strong reference and promotion significance for solving similar problems in other universities.
Keywords: Head Detection / AdaBoost / Students Counting / Classroom / Colleges and Universities
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