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  4. Vol 6, Issue 5, 2023

Abnormal Event Detection and Localization Based on Crowd Analysis in Video Surveillance

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DOI: 10.23977/jaip.2023.060508 | Downloads: 12 | Views: 368

Author(s)

Xu Xiao 1

Affiliation(s)

1 Department of Engineering Management, Jinan Engineering Vocational Technical College, Jinan, China

Corresponding Author

Xu Xiao

ABSTRACT

With the rapid development of economy and urban construction, abnormal events detection has arouse spread attention in need of public security. When an abnormal event occurs, the pedestrians in crowd escape or run instinctively which will lead to sharp change in the collectiveness feature and kinetic energy of crowd. This paper proposes a method based on the Collectiveness Energy Index (CEI) which combines the two features mentioned above to detect the abnormal events because it is not unreliable to utilize either of the two features singly. Besides, this paper also presents a means to locate abnormal behaviours in the anomalous scenes. Firstly, we obtain spatial coordinate of particles existed on individuals in each frame using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Then, the Collectiveness Energy Index (CEI) of each frame is calculated and compared with an adaptive threshold for abnormal events identification. In order to locate abnormal behaviours, this paper splits each input frame of video sequences into blocks without overlapping and then calculates the velocity and individual collectiveness of each block for classifying it as anomalous or not. Experiments conducted on UMN dataset and UCSD dataset verify the effectiveness and superiority of our detection and localization method.

KEYWORDS

Video surveillance, Abnormal event detection and localization, Collectiveness feature, Kinetic energy feature, Kanade-Lucas-Tomasi key point tracker

CITE THIS PAPER

Xu Xiao, Abnormal Event Detection and Localization Based on Crowd Analysis in Video Surveillance. Journal of Artificial Intelligence Practice (2023) Vol. 6: 58-65. DOI: http://dx.doi.org/10.23977/jaip.2023.060508.

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