• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Crowd escape event detection based on Direction-Collectiveness Model

Vol. 12, No.9, September 30, 2018
10.3837/tiis.2018.09.013 , Download Paper (Free):

Abstract

Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the ‘escape event’ occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.


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Cite this article

[IEEE Style]
Mengdi Wang, Faliang Chang and Youmei Zhang, "Crowd escape event detection based on Direction-Collectiveness Model," KSII Transactions on Internet and Information Systems, vol. 12, no. 9, pp. 4355-4374, 2018. DOI: 10.3837/tiis.2018.09.013

[ACM Style]
Wang, M., Chang, F., and Zhang, Y. 2018. Crowd escape event detection based on Direction-Collectiveness Model. KSII Transactions on Internet and Information Systems, 12, 9, (2018), 4355-4374. DOI: 10.3837/tiis.2018.09.013