Universiti Teknologi Malaysia Institutional Repository

Online video-based abnormal detection using highly motion techniques and statistical measures

Al-Dhamari, A. and Sudirman, R. and Mahmood, N. H. and Khamis, N. H. and Yahya, A. (2019) Online video-based abnormal detection using highly motion techniques and statistical measures. Telkomnika (Telecommunication Computing Electronics and Control), 17 (4). ISSN 1693-6930


Official URL: http://www.dx.doi.org/10.12928/TELKOMNIKA.v17i4.12...


At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for the accuracy while attaining simultaneously low values for the processing time.

Item Type:Article
Uncontrolled Keywords:abnormal detection, foreground features, motion analysis
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:91355
Deposited By: Narimah Nawil
Deposited On:30 Jun 2021 20:08
Last Modified:30 Jun 2021 20:08

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