A probabilistic exclusion principle for tracking multiple objects. (English) Zbl 1060.68629

MacCormick, John; Blake, Andrew

Int. J. Comput. Vis. 39, No. 1, 57-71 (2000).

Summary: 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. The 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.

MSC:

68T10	Pattern recognition, speech recognition
68U10	Computing methodologies for image processing

Keywords:

partitioned sampling; Monte Carlo; particle filter; tracking; multiple objects