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Proceedings Article

Automated-model-selection-based algorithm for tracking multiple nonlinear trajectories

[+] Author Affiliations
Mukesh A. Zaveri, Uday B. Desai, Shabbir N. Merchant

Indian Institute of Technology-Bombay (India)

Proc. SPIE 5298, Image Processing: Algorithms and Systems III, 242 (May 28, 2004); doi:10.1117/12.521174
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From Conference Volume 5298

  • Image Processing: Algorithms and Systems III
  • Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian
  • San Jose, CA | January 18, 2004

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 apriori. 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. Another problem with multiple trajectories tracking using particle filter is the data association, i.e. observation to track fusion. In this paper we propose a novel method, which overcomes the above problems. In a proposed method an interacting multiple model based approach is used along with particle filtering, which automates the model selection process for tracking an arbitrary trajectory. We have utilized nearest neighbor (NN) method for data association, which is fast and easy to implement.

© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Mukesh A. Zaveri ; Uday B. Desai and Shabbir N. Merchant
"Automated-model-selection-based algorithm for tracking multiple nonlinear trajectories", Proc. SPIE 5298, Image Processing: Algorithms and Systems III, 242 (May 28, 2004); doi:10.1117/12.521174; http://dx.doi.org/10.1117/12.521174


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