Special Section on Video Analytics for Public Safety

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

[+] Author Affiliations
Yi-Tung Chan

National Defense University, Chung Cheng Institute of Technology, School of Defense Science, Taoyuan City, Taiwan

Shuenn-Jyi Wang, Chung-Hsien Tsai

National Defense University, Chung Cheng Institute of Technology, Department of Computer Science and Information Engineering, Taoyuan City, Taiwan

J. Electron. Imaging. 26(5), 051402 (Jun 14, 2017). doi:10.1117/1.JEI.26.5.051402
History: Received November 4, 2016; Accepted March 6, 2017
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Abstract.  Public safety is a matter of national security and people’s livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method’s performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

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Citation

Yi-Tung Chan ; Shuenn-Jyi Wang and Chung-Hsien Tsai
"Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems", J. Electron. Imaging. 26(5), 051402 (Jun 14, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.5.051402


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