A Probabilistic Exclusion Principle for Tracking Multiple Objects
Public DepositedTracking 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.
Published as: MacCormick, John, and Andrew Blake. A Probabilistic Exclusion Principle for Tracking Multiple Objects.
International Journal of Computer Vision 39, no. 1 (2000): 57-71. This author post-print is made available on Dickinson Scholar with the permission of the publisher. For more information on the published version, visit Springer's Website.
MLA citation style (9th ed.)
A Probabilistic Exclusion Principle for Tracking Multiple Objects. dickinson.hykucommons.org/concern/generic_works/8722255c-8c19-4187-a4b6-1e9b1c3e44a0?locale=en.APA citation style (7th ed.)
A Probabilistic Exclusion Principle for Tracking Multiple Objects. https://dickinson.hykucommons.org/concern/generic_works/8722255c-8c19-4187-a4b6-1e9b1c3e44a0?locale=enChicago citation style (CMOS 17, author-date)
A Probabilistic Exclusion Principle for Tracking Multiple Objects. https://dickinson.hykucommons.org/concern/generic_works/8722255c-8c19-4187-a4b6-1e9b1c3e44a0?locale=en.Note: These citations are programmatically generated and may be incomplete.
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