DocumentCode :
253824
Title :
A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions
Author :
Menglong Yang ; Yiguang Liu ; Longyin Wen ; Zhisheng You ; Li, Stan Z.
Author_Institution :
Key Lab. of Fundamental Synthetic Vision Graphics & Image for Nat. Defense, Sichuan Univ., Chengdu, China
fYear :
2014
fDate :
23-28 June 2014
Firstpage :
1298
Lastpage :
1305
Abstract :
Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. This paper presents a novel probability framework for multitarget tracking with mutual occlusions. The primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. The occlusion variable describes occlusion states of the targets. This forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and oc-cluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a newmove type called "occlusion state update" is presented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.
Keywords :
Markov processes; object tracking; probability; random processes; target tracking; MRF; Markov random field; RJMCMC-based algorithm; cascading occlusion types; circular occlusions type; likelihood model; multitarget tracking; mutual occlusions; occlusion priori model; occlusion state update; occlusion states; probability framework; target position deviation; track loss; tracking failures; vectorial occlusion variable; Approximation algorithms; Cameras; Computational modeling; Probabilistic logic; Proposals; Target tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
Type :
conf
DOI :
10.1109/CVPR.2014.169
Filename :
6909565
Link To Document :
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