DocumentCode :
114664
Title :
Macroscopic analysis of crowd motion in video sequences
Author :
Nakhmani, Arie ; Surana, Amit ; Tannenbaum, Allen
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Alabama at Birmingham, Birmingham, AL, USA
fYear :
2014
fDate :
15-17 Dec. 2014
Firstpage :
1822
Lastpage :
1827
Abstract :
We use dynamic active contours driven by optimal mass transport optical flow to detect crowd behaviors, in particular crowd merging, splitting and collision events. The overall framework is variational, and thus one could very naturally formulate functionals which include geometric active contours together with optical flow ideas. This allows to fuse temporal and intensity distribution information explicitly into a single framework. From a networking point of view, we consider here macroscopic models as opposed to microscopic or mesoscopic approaches to crowd dynamics. Our experiments show high detection rate of macro crowd behaviors with complicated real world scenarios.
Keywords :
computational geometry; image fusion; image sequences; video signal processing; video surveillance; crowd behavior detection; crowd collision event; crowd dynamics; crowd merging event; crowd motion; crowd splitting event; detection rate; dynamic active contours; explicit intensity distribution information fusion; explicit temporal distribution information fusion; geometric active contours; macrocrowd behaviors; macroscopic analysis; optimal mass transport optical flow; real world scenarios; variational framework; video sequences; Active contours; Computational modeling; Level set; Mathematical model; Motion segmentation; Optical imaging; Tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-1-4799-7746-8
Type :
conf
DOI :
10.1109/CDC.2014.7039663
Filename :
7039663
Link To Document :
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