NDLI: Tracking of point targets in IR image sequence using multiple model based particle filtering and MRF based data association

Content Provider	IEEE Xplore Digital Library
Author	Zaveri, M.A. Merchant, S.N. Desai, U.B.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., IIT Bombay, India (Zaveri, M.A.; Merchant, S.N.; Desai, U.B.)
Abstract	Particle filtering is being investigated extensively due to its important feature of target tracking based on nonlinear and non-Gaussian model. It tracks a trajectory with a known model at a given time. It means that particle filter tracks an arbitrary trajectory only if the time instant when trajectory switches from one model to another model is known a priori. Because of this reason particle filter is not able to track any arbitrary trajectory where transition from one model to another model is not known. For real world application, trajectory is always random in nature and may follow more than one model. In this paper we propose a novel method, which overcomes the above problem. In the proposed method a multiple model based approach is used along with particle filtering, which automates the model selection process for tracking an arbitrary trajectory. In the proposed approach, there is no need to have a priori information about the exact model that a target may follow. For data association, Markov random field (MRF) based method has been utilized. It allows us to exploit the neighborhood concept for data association, i.e. the association of a measurement influences an association of its neighbor measurement.
Starting Page	729
Ending Page	732
File Size	1034599
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1333876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Particle tracking Image sequences Filtering Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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