Radiometry, Infrared Systems, Tracking

Tracking of multiple-point targets using multiple-model-based particle filtering in infrared image sequence

[+] Author Affiliations
Mukesh A. Zaveri

SPANN Lab, Electrical Engineering Dept., IIT-Bombay, Mumbai-400076 India

S. N. Merchant

SPANN Lab, Electrical Engineering Dept., IIT-Bombay, Mumbai-400076 India

Uday B. Desai

SPANN Lab, Electrical Engineering Dept., IIT-Bombay, Mumbai-400076 India

Opt. Eng. 45(5), 056404 (May 24, 2006). doi:10.1117/1.2205858
History: Received December 28, 2004; Revised August 18, 2005; Accepted October 11, 2005; Published May 24, 2006
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Particle filtering is investigated extensively due to its importance in target tracking for nonlinear and non-Gaussian models. A particle filter can track an arbitrary trajectory only if the target dynamics models are known and the time instant when trajectory switches from one model to another model is known a priori. In real applications, it is unlikely to meet both these conditions. We propose a novel method that overcomes the lack of this knowledge. In the proposed method, an interacting multiple-model-based approach is exploited along with particle filtering. Moreover, we automate the model selection process for tracking an arbitrary trajectory. In the proposed approach, a priori information about the exact model that a target may follow is not required. Another problem with multiple trajectory tracking using a particle filter is data association, namely, observation to track fusion. For data association, we use three methods. In the first case, an implicit observation to track assignment is performed using a nearest neighbor (NN) method for data association; this is fast and easy to implement. In the second method, the uncertainty about the origin of an observation is overcome by using a centroid of measurements to evaluate weights for particles as well as to calculate the likelihood of a model. In the third method, a Markov random field (MRF)-based method is used. The MRF method enables us to exploit the neighborhood concept for data association, i.e., the association of a measurement influences an association of its neighboring measurement.

© 2006 Society of Photo-Optical Instrumentation Engineers

Citation

Mukesh A. Zaveri ; S. N. Merchant and Uday B. Desai
"Tracking of multiple-point targets using multiple-model-based particle filtering in infrared image sequence", Opt. Eng. 45(5), 056404 (May 24, 2006). ; http://dx.doi.org/10.1117/1.2205858


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