Tracking multiple objects with multiple sensors is widely recognized to be much more complex than the single-sensor scenario. This contribution proposes a computationally tractable multi-sensor multi-target tracker. Based on Bayes equation and multi-senor observation model, a new corrector for multi-senor is derived. To lower the complexity of update operation, a parallel track-to-measurement association strategy is applied to the corrector. Hypotheses truncation scheme along with first-moment approximation of multi-target density are also employed to improve the tracking efficiency. The tracker is applied to a couple-sensor scenario. Experiment results validate the advantages of proposed method compared to the standard single-sensor δ-generalized labeled multi-Bernoulli filter and the iterated-corrector probability hypothesis density filter.
Centralized multi-sensor multi-target tracking with labeled random finite set
2017-03-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Centralized multi-sensor multi-target tracking with labeled random finite set
Online Contents | 2017
|Multi-object tracking using random finite sets
TIBKAT | 2014
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