Open AccessAutonomous Abnormal Behaviour Detection Using Trajectory Analysis
Author(s) -
Muhammed Shuaau,
Ka Fei Thang,
Nai Shyan Lai
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i4.pp2403-2415
Subject(s) - anomaly detection , computer science , trajectory , context (archaeology) , kalman filter , artificial intelligence , computer vision , real time computing , geography , physics , archaeology , astronomy
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%.