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Probabilistic Object Tracking Using Multiple Features

D. Serby, E. Koller-Meier and L. Van Gool
International Conference on Pattern Recognition (ICPR04)
Cambridge, August 2004

Abstract

We present a generic tracker which can handle a variety of different objects. For this purpose, groups of low-level features like interest points, edges, homogeneous and textured regions, are combined on a flexible and opportunistic basis. They sufficiently characterize an object and allow robust tracking as they are complementary sources of information which describe both the shape and the appearance of anobject. These low-level features are integrated into a particle filter framework as this has proven very successful for non-linear and non-Gaussian estimation problems. In this paper we concentrate on rigid objects under affine transformations. Results on real-world scenes demonstrate the performance of the proposed tracker.


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@InProceedings{eth_biwi_00314,
  author = {D. Serby and E. Koller-Meier and L. Van Gool},
  title = {Probabilistic Object Tracking Using Multiple Features},
  booktitle = {International Conference on Pattern Recognition (ICPR04)},
  year = {2004},
  month = {August},
  pages = {184-187},
  volume = {2},
  keywords = {tracking, particle filter, multiple features}
}