International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Abnormal Crowd Behavior Detection Using Heuristic Search and Motion Awareness

  • Usman, Imran (College of Computing and Informatics, Saudi Electronic University) ;
  • Albesher, Abdulaziz A. (College of Computing and Informatics, Saudi Electronic University)
  • Received : 2021.04.05
  • Published : 2021.04.30
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Abstract

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

Keywords

Acknowledgement

The authors highly acknowledge the research support provided by the Deanship of Scientific Research (DSR) at Saudi Electronic University, Riyadh, Saudi Arabia, through the approved project titled "Abnormal Crowd Behavior Detection for Time Varying Moving Sequences" with the research number 7661-CAI-2019-3-1-A.

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