A Complete Derivation Of The Association Log-Likelihood Distance For Multi-Object Tracking
This work addresses a specific bottleneck in multi-object tracking for applications like surveillance or robotics, but it is incremental as it builds on existing multiple hypothesis tracking methods.
The paper tackles the problem of measurement-to-track association in multi-object tracking by deriving an association log-likelihood distance from global association hypotheses and comparing it to the commonly used Mahalanobis distance. The result shows that the new distance performs better on average in Monte-Carlo simulations, confirming it as a more fundamental approach.
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.