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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 LeckieCrowd 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.
History
Event
Australian Pattern Recognition Society. Conference (2018 : Canberra, A.C.T.)Series
Australian Pattern Recognition Society ConferencePagination
1 - 8Publisher
Institute of Electrical and Electronics EngineersLocation
Canberra, A.C.T.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2018-12-10End date
2018-12-13ISBN-13
9781538666029Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
M Murshed, M Paul, M Asikuzzaman, M Pickering, A Natu, A Robles-Kelly, S You, L Zheng, A RahmanTitle of proceedings
DICTA 2018 : Proceedings of the 2018 Digital Image Computing: Techniques and ApplicationsUsage metrics
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