Probabilistic Data Association Methods for Tracking Complex Visual Objects

Abstract

ÐWe describe a framework that explicitly reasons about data association to improve tracking performance in many difficult visual environments. A hierarchy of tracking strategies results from ascribing ambiguous or missing data to: 1) noise-like visual occurrences, 2) persistent, known scene elements (i.e., other tracked objects), or 3) persistent, unknown… (More)
DOI: 10.1109/34.927458

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