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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Yogesh Golhar
St. Vincent Palloti College of Engineering and Technology
Computer Science & IT Engineering

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Novel approach for pedestrian unusual activity detection in academic environment Kamal Omprakash Hajari; Ujwalla Haridas Gawande; Yogesh Golhar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, velocity, and direction, as well as their intercommunication within a frame. We also use these motion features to detect both global and local anomalous behaviors within the frame. The proposed approach is validated on a newly built proposed student behavior database and three additional publicly available benchmark datasets. When compared to state-of-the-art techniques, the experimental results reveal a considerable performance improvement in anomalous activity recognition. Finally, we summarize and discuss future research directions.
Co-Authors Kamal Omprakash Hajari Ujwalla Haridas Gawande