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arXiv · 1508.04124

A Complete Derivation Of The Association Log-Likelihood Distance For Multi-Object Tracking

Richard Altendorfer·Sebastian Wirkert
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Abstract

The Mahalanobis distance is commonly used in multi-object trackers for measurement-to-track association. Starting with the original definition of the Mahalanobis distance we review its use in association. Given that there is no principle in multi-object tracking that sets the Mahalanobis distance apart as a distinguished statistical distance we revisit the global association hypotheses of multiple hypothesis tracking as the most general association setting. Those association hypotheses induce a distance-like quantity for assignment which we refer to as association log-likelihood distance. We compare the ability of the Mahalanobis distance to the association log-likelihood distance to yield correct association relations in Monte-Carlo simulations. It turns out that on average the distance based on association log-likelihood performs better than the Mahalanobis distance, confirming that the maximization of global association hypotheses is a more fundamental approach to association than the minimization of a certain statistical distance measure.

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BibTeXRIS

Richard Altendorfer, Sebastian Wirkert. 2015-08-17. A Complete Derivation Of The Association Log-Likelihood Distance For Multi-Object Tracking. https://doi.org/10.1109/ivs.2016.7535395

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