Using video analytics to give insights about events
happening in classroom is a very important task in classroom
surveillance systems. This paper proposes a new algorithmic
frameworkto identify abrupt changes in a class room video and
thenevaluate the attention level of students.The proposed
algorithm is implemented with and without video key frame
extraction approaches. The SSIM (Structural Similarity Index)
approach for key frame extraction is used in this study. After
extracting the key frames, the detection of face and upper body of
the students to evaluate their attention level is performed on the
key frames. The results comparing thealgorithms with and
without SSIM reveals that the SSIM based algorithm gives better
results. The algorithmic design of the proposed approach, the
results obtained and sample cases are presented in this paper.
S. Ahmed, Krishnnan, N., Ganta, T., and Jeyakumar, G., “A Video Analytics System for Class Room Surveillance Applications”, International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 5S3, 2019.