NDLI: A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

Content Provider	IEEE Xplore Digital Library
Author	Menglong Yang Yiguang Liu Longyin Wen Zhisheng You Li, S.Z.
Copyright Year	2014
Description	Author affiliation: Key Lab. of Fundamental Synthetic Vision Graphics & Image for Nat. Defense, Sichuan Univ., Chengdu, China (Menglong Yang; Yiguang Liu) \|\| Center for Biometrics & Security Res., Nat. Lab. of Pattern Recognition, China (Longyin Wen; Zhisheng You; Li, S.Z.)
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.
Starting Page	1298
Ending Page	1305
File Size	1664746
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Probabilistic logic Approximation algorithms Cameras Proposals Computational modeling
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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