A labeled random finite set online multi-object tracker for video data

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A labeled random finite set online multi-object tracker for video data

This paper proposes an online multi-object tracking algorithm for image observations using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, handling of false positives, false negatives and occlusion into a single recursion. This is achieved by modeling t...

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Bibliographic Details
Main Authors:	Kim, Du Yong, Vo, Ba-Ngu, Vo, Ba Tuong, Jeon, M.
Format:	Journal Article
Published:	Elsevier 2019
Online Access:	http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/74333

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