1 May 2006 Tracking of multiple-point targets using multiple-model-based particle filtering in infrared image sequence
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Optical Engineering, 45(5), 056404 (2006). doi:10.1117/1.2205858
Abstract
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.
Mukesh A. Zaveri, Shabbir N. Merchant, Uday B. Desai, "Tracking of multiple-point targets using multiple-model-based particle filtering in infrared image sequence," Optical Engineering 45(5), 056404 (1 May 2006). http://dx.doi.org/10.1117/1.2205858
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KEYWORDS
Particles

Particle filters

Data modeling

Optical tracking

Electronic filtering

Nonlinear filtering

Monte Carlo methods

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