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.

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.