An adaptive sample count particle filter

  • Authors:

  • Waqas Hassan;Nagachetan Bangalore;Philip Birch;Rupert Young;Chris Chatwin

  • Affiliations:

  • School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QT, United Kingdom;School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QT, United Kingdom;School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QT, United Kingdom;School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QT, United Kingdom;School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QT, United Kingdom

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2012

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Abstract

The particle filter technique has been used extensively over the past few years to track objects in challenging environments. Due to its nonlinear nature and the fact that it does not assume a Gaussian probability density function it tends to outperform other available tracking methods. A novel adaptive sample count particle filter (ASCPF) tracking method is presented in this paper for which the main motivation is to accurately track an object in crowded scenes using fewer particles and hence with reduced computational overhead. Instead of taking a fixed number of particles, a particle range technique is used where an upper and lower bound for the range is initially identified. Particles are made to switch between an active and inactive state within this identified range. The idea is to keep the number of active particles to a minimum and only to increase this as and when required. Active contours are also utilized to determine a precise area of support around the tracked object from which the color histograms used by the particle filter can be accurately calculated. This, together with the variable particle spread, allows a more accurate proposal distribution to be generated while using less computational resource. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster.