DocumentCode
2611624
Title
Observation-Switching Linear Dynamic Systems for Tracking Humans Through Unexpected Partial Occlusions by Scene Objects
Author
Peursum, Patrick ; Venkatesh, Svetha ; West, Geoff
Author_Institution
Dept. of Comput., Curtin Univ. of Technol., Perth, WA
Volume
4
fYear
0
fDate
0-0 0
Firstpage
929
Lastpage
934
Abstract
This paper focuses on the problem of tracking people through occlusions by scene objects. Rather than relying on models of the scene to predict when occlusions will occur as other researchers have done, this paper proposes a linear dynamic system that switches between two alternatives of the position measurement in order to handle occlusions as they occur. The filter automatically switches between a foot-based measure of position (assuming z = 0) to a head-based position measure (given the person´s height) when an occlusion of the person´s lower body occurs. No knowledge of the scene or its occluding objects is used. Unlike similar research (Fleuret et al., 2005; Zhao and Nevatia, 2004), the approach does not assume a fixed height for people and so is able to track humans through occlusions even when they change height during the occlusion. The approach is evaluated on three furnished scenes containing tables, chairs, desks and partitions. Occlusions range from occlusions of legs, occlusions whilst being seated and near-total occlusions where only the person´s head is visible. Results show that the approach provides a significant reduction in false-positive tracks in a multi-camera environment, and more than halves the number of lost tracks in single monocular camera views
Keywords
computer vision; feature extraction; filtering theory; hidden feature removal; target tracking; filtering; foot-based measure; head-based position measure; human tracking; observation-switching linear dynamic systems; occlusion handling; partial occlusions; position measurement; scene objects; Australia; Cameras; Humans; Layout; Measurement standards; Nonlinear filters; Particle filters; Position measurement; Predictive models; Switches;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location
Hong Kong
ISSN
1051-4651
Print_ISBN
0-7695-2521-0
Type
conf
DOI
10.1109/ICPR.2006.888
Filename
1699992
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