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A probabilistic exclusion principle for tracking multiple objects
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DOI：10.1023/A:1008122218374.png)
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Abstract
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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.
Keywords:
partitioned sampling
Monte Carlo
particle filter
tracking
multiple objects
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