Deakin University

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Cluster-based crowd movement behavior detection

conference contribution
posted on 2018-01-01, 00:00 authored by M Yang, L Rashidi, A S Rao, Sutharshan RajasegararSutharshan Rajasegarar, M Ganji, M Palaniswami, C Leckie
Crowd behaviour monitoring and prediction is an important research topic in video surveillance that has gained increasing attention. In this paper, we propose a novel architecture for crowd event detection, which comprises methods for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events. In our proposed framework, we use clusters to represent the groups of objects/people present in the scene. We then extract the movement patterns of the various groups of objects over the video sequence to detect movement patterns. We define several crowd events and propose a methodology to detect the change point of the group events over time. We evaluated our scheme using six video sequences from benchmark datasets, which include events such as walking, running, global merging, local merging, global splitting and local splitting. We compared our scheme with state of the art methods and showed the superiority of our method in accurately detecting the crowd behavioral changes.



Australian Pattern Recognition Society. Conference (2018 : Canberra, A.C.T.)


Australian Pattern Recognition Society Conference


1 - 8


Institute of Electrical and Electronics Engineers


Canberra, A.C.T.

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed

Copyright notice

2018, IEEE


M Murshed, M Paul, M Asikuzzaman, M Pickering, A Natu, A Robles-Kelly, S You, L Zheng, A Rahman

Title of proceedings

DICTA 2018 : Proceedings of the 2018 Digital Image Computing: Techniques and Applications