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A Complete Variational Tracker
Ryan D Turner · Steven Bottone · Bhargav Avasarala

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D

We introduce a novel probabilistic tracking algorithm that incorporates combinatorial data association constraints and model-based track management using variational Bayes. We use a Bethe entropy approximation to incorporate data association constraints that are often ignored in previous probabilistic tracking algorithms. Noteworthy aspects of our method include a model-based mechanism to replace heuristic logic typically used to initiate and destroy tracks, and an assignment posterior with linear computation cost in window length as opposed to the exponential scaling of previous MAP-based approaches. We demonstrate the applicability of our method on radar tracking and computer vision problems.

Author Information

Ryan D Turner (Northrop Grumman)
Steven Bottone (Northrop Grumman)
Bhargav Avasarala (Northrop Grumman Corp)

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