DocumentCode :
2643054
Title :
Crowd Analysis at Mass Transit Sites
Author :
Kilambi, Prahlad ; Masoud, Osama ; Papanikolopoulos, Nikolaos
Author_Institution :
Dept. of Comput. Sci. & Eng., Minnesota Univ., Twin Cities, MN
fYear :
2006
fDate :
17-20 Sept. 2006
Firstpage :
753
Lastpage :
758
Abstract :
We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in realtime. There are no constraints on camera placement. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Results are provided for groups of various sizes moving in an unconstrained fashion in crowded scenes
Keywords :
Kalman filters; estimation theory; parameter estimation; traffic engineering computing; Kalman filtering; camera calibration; camera placement; crowd analysis; mass transit sites; parameter estimation; Calibration; Cameras; Cities and towns; Computer science; Filtering; Head; Kalman filters; Layout; Parameter estimation; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
Conference_Location :
Toronto, Ont.
Print_ISBN :
1-4244-0093-7
Electronic_ISBN :
1-4244-0094-5
Type :
conf
DOI :
10.1109/ITSC.2006.1706832
Filename :
1706832
Link To Document :
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