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Proceedings Paper

Symmetrizing measurement equations for association-free multi-target tracking via point set distances
Author(s): Uwe D. Hanebeck; Marcus Baum; Peter Willett
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Paper Abstract

We are tracking multiple targets based on noisy measurements. The targets are labeled, the measurements are unlabeled, and the association of measurements to targets is unknown. Our goal is association-free tracking, so the associations will never be determined as this is costly and impractical in many scenarios. By employing a permutation-invariant and differentiable point set distance measure, we derive a modified association-free multi-target measurement equation. It maintains the target identities but is invariant to permutations in the unlabeled measurements. Based on this measurement equation, we derive an efficient sample-based association-free multi-target Kalman filter. The proposed new approach is straightforward to implement and scalable.

Paper Details

Date Published: 2 May 2017
PDF: 14 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020006 (2 May 2017); doi: 10.1117/12.2266988
Show Author Affiliations
Uwe D. Hanebeck, Karlsruher Institut für Technologie (Germany)
Marcus Baum, Georg-August-Univ. Göttingen (Germany)
Peter Willett, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

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