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Exploring Techniques for Abnormal Event Detection in Video Surveillance

Varsha Anil Kshirsagar,
Shou-Chih Lo,
Guanling Lee,

Abstract


This study explores and evaluates the effectiveness of various abnormal event detection techniques in video surveillance, addressing challenges such as intrusions, accidents, and suspicious activities. Through a systematic review of related papers, the study reveals the prevalence of traditional methods like background subtraction and motion detection despite their limitations in complex scenarios. It highlights the increasing use of deep learning techniques, particularly CNNs and RNNs, which show promise but require substantial labeled data. The findings underscore the importance of selecting proper detection techniques based on specific surveillance scenarios and emphasize the need for extensive labeled datasets for deep learning methods. The originality of this study lies in its comprehensive review and comparison of various abnormal event detection techniques, providing valuable insights and practical implications for advancing video surveillance systems.

Keywords


Abnormal event detection, Video surveillance, Object tracking, Machine learning

Citation Format:
Varsha Anil Kshirsagar, Shou-Chih Lo, Guanling Lee, "Exploring Techniques for Abnormal Event Detection in Video Surveillance," Journal of Internet Technology, vol. 25, no. 5 , pp. 781-787, Sep. 2024.

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