Paper Info
Title
Occlusion reasoning for tracking multiple people
Abstract
Occlusion reasoning is one of the most challenging issues in visual surveillance. In this letter, we propose a new approach for reasoning about occlusions between multiple people. In our approach, occlusion relationships between people are explicitly defined and deduction of the occlusion relationships is integrated into the whole tracking framework. The prior knowledge is supplied by a set of models which include a 2-D elliptical shape model, a spatial-color mixture of Gaussians appearance model, and a motion model with constant velocity. An observation likelihood function is constructed based on the similarity between the observations and the object appearance models with given states. The occlusion relationships are deduced from the current states of the objects and the current observations, using the observation likelihood function. The previous occlusion relationships are not required for deducing the current occlusion relationships. The problem of tracking and occlusion reasoning for more than two people is formulated mathematically, and a solution is proposed based on particle filtering. Experimental results on several real video sequences from indoor and outdoor scenes show the effectiveness of our approach.
Year
DOI
Venue
2009
10.1109/TCSVT.2008.2009249
IEEE Trans. Circuits Syst. Video Techn.
Keywords
Field
DocType
indexing terms,object tracking,shape,layout,particle filter,calibration,filtering,gaussian processes,particle filtering,computer graphics,mixture of gaussians
Similitude,Computer vision,Occlusion,Likelihood function,Pattern recognition,Computer science,Particle filter,Active appearance model,Video tracking,Artificial intelligence,Gaussian process,Mixture model
Journal
Volume
Issue
ISSN
19
1
1051-8215
Citations 
PageRank 
References 
23
0.93
19
Authors
4
Name
Order
Citations
PageRank
Weiming Hu15300261.38
Xue Zhou219411.81
Min Hu31649.37
S. J. Maybank42468226.95