NDLI: Spatial and temporal variations of feature tracks for crowd behavior analysis

Content Provider	Springer Nature Link
Author	Fradi, Hajer Dugelay, Jean Luc
Copyright Year	2015
Abstract	The study of crowd behavior in public areas or during some public events is receiving a lot of attention in security community to detect potential risk and to prevent overcrowd. In this paper, we propose a novel approach for change detection, event recognition and characterization in human crowds. It consists of modeling time-varying dynamics of the crowd using local features. It also involves a feature tracking step which allows excluding feature points on the background and extracting long-term trajectories. This process is favourable for the later crowd event detection and recognition since the influence of features irrelevant to the underlying crowd is removed and the tracked features undergo an implicit temporal filtering. These feature tracks are further employed to extract regular motion patterns such as speed and flow direction. In addition, they are used as an observation of a probabilistic crowd function to generate fully automatic crowd density maps. Finally, the variation of these attributes (local density, speed, and flow direction) in time is employed to determine the ongoing crowd behaviors. The experimental results on two different datasets demonstrate the effectiveness of our proposed approach for early detection of crowd change and accurate results for event recognition and characterization.
Starting Page	307
Ending Page	317
Page Count	11
File Format	PDF
ISSN	17837677
Journal	Journal on Multimodal User Interfaces
Volume Number	10
Issue Number	4
e-ISSN	17838738
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-05-30
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Crowd tracking Local features Event recognition Change detection User Interfaces and Human Computer Interaction Signal,Image and Speech Processing Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Signal Processing Human-Computer Interaction

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