NDLI: Robust multiple object tracking by detection with interacting Markov chain Monte Carlo

Content Provider	IEEE Xplore Digital Library
Author	Santhoshkumar, S. Karthikeyan, S. Manjunath, B.S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, Santa Barbara, CA, USA (Santhoshkumar, S.; Karthikeyan, S.; Manjunath, B.S.)
Abstract	This paper presents a novel and computationally efficient multi-object tracking-by-detection algorithm with interacting particle filters. The proposed online tracking methodology could be scaled to hundreds of objects and could be completely parallelized. For every object, we have a set of two particle filters, i.e. local and global. The local particle filter models the local motion of the object. The global particle filter models the interaction with the other objects and scene. These particle filters are integrated into a unified Interacting Markov Chain Monte Carlo (IMCMC) framework. The local particle filter improves its performance by interacting with the global particle filter while they both are run in parallel. We indicate the manner in which we bring in object interaction and domain specific information into account by using global filters without further increase in complexity. Most importantly, the complexity of the proposed methodology varies linearly in the number of objects. We validated the proposed algorithms on two completely different domains 1) Pedestrian Tracking in urban scenarios 2) Biological cell tracking (Melanosomes). The proposed algorithm is found to yield favorable results compared to the existing algorithms.
Starting Page	2953
Ending Page	2957
File Size	806439
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738608
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle Filters Tracking Detection
Content Type	Text
Resource Type	Article

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