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A Video Analytics Based Solution for Detecting the Attention Level of the Students in Class Rooms

Publication Type : Conference Paper

Publisher : 10th International Conference on Cloud Computing

Source : 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence) (2020)

Url : https://ieeexplore.ieee.org/abstract/document/9057967

Keywords : Cameras, Classification algorithms, classroom surveillance, classroom videos, Computer aided instruction, Drowsiness detection, face, Face recognition, Facial Expression Detection, facial expressions, Feature extraction, gaze detection, gaze tracking, key frame extraction, live video, Object Detection, Psychological state, Psychology, Streaming media, structural similarity index method, student attention level, Student behavior, Surveillance, Video analytics, video cameras, Video signal processing, Video surveillance

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2020

Abstract : Classroom surveillance, using video cameras, affords enhanced understanding of student behavior. This paper proposes a new algorithmic framework to evaluate the attention level of students, from classroom videos. The live video of a class room, when a teacher is delivering the lecture, is the input to the proposed framework. This framework identifies the key frames from the video and then detects the attention level of a particular student. The paper perused the Structural Similarity Index Method (SSIM) to discern key frames in a video. Detection of drowsiness is then performed to deduce whether or not the student is sleepy. Scrutiny of facial expressions is carried out, to perceive the psychological state of the student in the classroom. Finally, detection of gaze is carried out to examine whether or not the student's attention is on the black board. The algorithmic design for the proposed approach, the results obtained and the sample test cases are presented in this paper.

Cite this Research Publication : N. Krishnnan, S. Ahmed, Ganta, T., and Dr. Jeyakumar G., “A Video Analytics Based Solution for Detecting the Attention Level of the Students in Class Rooms”, in 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence), 2020.

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