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A Probabilistic Exclusion Principle for Tracking Multiple Objects

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Abstract

Tracking multiple targets is a challenging problem, especially when the targets are “identical”, in the sense that the same model is used to describe each target. In this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.

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Authors and Affiliations

  1. Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK

    John MacCormick

  2. Microsoft Research Limited, St George House, 1 Guildhall Street, Cambridge, CB2 3NH, UK

    Andrew Blake

Authors
  1. John MacCormick
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  2. Andrew Blake
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MacCormick, J., Blake, A. A Probabilistic Exclusion Principle for Tracking Multiple Objects. International Journal of Computer Vision 39, 57–71 (2000). https://doi.org/10.1023/A:1008122218374

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