This paper introduces an equivalence-class approach to multi-target tracking. The approach seeks to address a fundamental limitation in multiple-hypothesis tracking: its selection (albeit with some delay and after reasoning over multiple hypotheses) of a unique global hypothesis. For some problems, the resulting tracking solution does a poor job with respect to metrics of interest. We seek instead to identify a class of similar hypotheses that have a larger aggregate likelihood than the maximum likelihood solution and, more importantly, whose members provide an improved tracking solution. Correspondingly, we introduce the Equivalence-Class MHT (ECMHT) and show its performance benefits in two-target tracking scenarios with a network of synchronous sensors.1 2
An equivalence-class approach to multiple-hypothesis tracking
2012-03-01
462495 byte
Conference paper
Electronic Resource
English
Multiple Hypothesis Tracking for Multiple Target Tracking
Online Contents | 2004
|TPP1.13 Object Tracking and Classification using a Multiple Hypothesis Approach
British Library Conference Proceedings | 2004
|Multi-robot multiple hypothesis tracking for pedestrian tracking
British Library Online Contents | 2012
|