Multi-dimensional visual tracking using scatter search particle filter

  • Authors:

  • J. J. Pantrigo;A. Sánchez;A. S. Montemayor;A. Duarte

  • Affiliations:

  • Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, c/Tulipán s/n, 28933 Móstoles, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, c/Tulipán s/n, 28933 Móstoles, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, c/Tulipán s/n, 28933 Móstoles, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, c/Tulipán s/n, 28933 Móstoles, Spain

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2008

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Abstract

Multi-dimensional visual tracking (MVT) problems include visual tracking tasks where the system state is defined by a high number of variables corresponding to multiple model components and/or multiple targets. A MVT problem can be modeled as a dynamic optimization problem. In this context, we propose an algorithm which hybridizes particle filters (PF) and the scatter search (SS) metaheuristic, called scatter search particle filter (SSPF), where the optimization strategies from SS are embedded into the PF framework. Scatter search is a population-based metaheuristic successfully applied to several complex combinatorial optimization problems. The most representative optimization strategies from SS are both solution combination and solution improvement. Combination stage enables the solutions to share information about the problem to produce better solutions. Improvement stage makes also possible to obtain better solutions by exploring the neighborhood of a given solution. In this paper, we have described and evaluated the performance of the scatter search particle filter (SSPF) in MVT problems. Specifically, we have compared the performance of several state-of-the-art PF-based algorithms with SSPF algorithm in different instances of 2D articulated object tracking problem and 2D multiple object tracking. Some of these instances are from the CVBase'06 standard database. Experimental results show an important performance gain and better tracking accuracy in favour of our approach.