An efficient particle filter for color-based tracking in complex scenes

  • Authors:

  • Jesus Martinez-del-Rincon;Carlos Orrite-Urunuela;J. Elias Herrero-Jaraba

  • Affiliations:

  • Computer Vision Lab, Aragon Institute for Engineering Research, University of Zaragoza - Spain;Computer Vision Lab, Aragon Institute for Engineering Research, University of Zaragoza - Spain;Computer Vision Lab, Aragon Institute for Engineering Research, University of Zaragoza - Spain

  • Venue:
  • AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
  • Year:
  • 2007

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Abstract

In this paper, we introduce an efficient method for particle selection in tracking objects in complex scenes. Firstly, we improve the proposal distribution function of the tracking algorithm, including current observation, reducing the cost of evaluating particles with a very low likelihood. In addition, we use a partitioned sampling approach to decompose the dynamic state in several stages. It enables to deal with high-dimensional states without an excessive computational cost. To represent the color distribution, the appearance of the tracked object is modelled by sampled pixels. Based on this representation, the probability of any observation is estimated using non-parametric techniques in color space. As a result, we obtain a Probability color Density Image (PDI) where each pixel points its membership to the target color model. In this way, the evaluation of all particles is accelerated by computing the likelihood p(z∣x) using the Integral Image of the PDI.