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All Journal International Journal of Electrical and Computer Engineering
Nai Shyan Lai
Asia Pacific University of Technology & Innovation, Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Autonomous Abnormal Behaviour Detection Using Trajectory Analysis Muhammed Shuaau; Ka Fei Thang; Nai Shyan Lai
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.626 KB) | DOI: 10.11591/ijece.v9i4.pp2403-2415

Abstract

Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%.
Co-Authors Ka Fei Thang Muhammed Shuaau