NDLI: Beyond particle flow: Bag of Trajectory Graphs for dense crowd event recognition

Content Provider	IEEE Xplore Digital Library
Author	Yanhao Zhang Lei Qin Hongxun Yao Pengfei Xu Qingming Huang
Copyright Year	2013
Description	Author affiliation: Harbin Inst. of Technol., Harbin, China (Yanhao Zhang; Hongxun Yao; Pengfei Xu; Qingming Huang) \|\| Inst. of Comput. Technol., Beijing, China (Lei Qin)
Abstract	In this paper, a novel crowd behavior representation, Bag of Trajectory Graphs (BoTG), is presented for dense crowd event recognition. To overcome huge loss of crowd structure and variability of motion in previous particle flow based methods, we design group-level representation beyond particle flow. From the observation that crowd particles are composed of atomic subgroups corresponding to informative behavior patterns, particle trajectories which simulate motion of individuals will be clustered to form groups at the first step. Then we connect nodes in each group as a trajectory graph and discover informative features to depict the graphs. A clip of crowd event can be further described by Bag of Trajectory Graphs (BoTG)-occurrences of behavior patterns, which provides critical clues for categorizing specific crowd event and detecting abnormality. The experimental results of abnormality detection and event recognition on public datasets demonstrate the effectiveness of our proposed BoTG on characterizing the group behaviors in dense crowd.
Starting Page	3572
Ending Page	3576
File Size	1020869
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Event Recognition Bag of Trajectory Graphs Attributes Crowd Behavior
Content Type	Text
Resource Type	Article

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