ML Anthology

A Probabilistic Exclusion Principle for Tracking Multiple Objects

John MacCormick, Andrew Blake
ICCV 1999 pp. 572-578
doi:10.1109/ICCV.1999.791275 /iccv/1999/maccormick1999iccv-probabilistic/

Abstract

Tracking multiple targets whose models are indistinguishable is a challenging problem. Simply instantiating several independent I-body trackers is not an adequate solution, because the independent trackers can 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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Cite

Text

MacCormick and Blake. "A Probabilistic Exclusion Principle for Tracking Multiple Objects." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.791275

Markdown

[MacCormick and Blake. "A Probabilistic Exclusion Principle for Tracking Multiple Objects." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/maccormick1999iccv-probabilistic/) doi:10.1109/ICCV.1999.791275

BibTeX

@inproceedings{maccormick1999iccv-probabilistic,
  title     = {{A Probabilistic Exclusion Principle for Tracking Multiple Objects}},
  author    = {MacCormick, John and Blake, Andrew},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1999},
  pages     = {572-578},
  doi       = {10.1109/ICCV.1999.791275},
  url       = {https://mlanthology.org/iccv/1999/maccormick1999iccv-probabilistic/}
}