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Multiple human tracking in RGB-depth data: A survey
Authors
Adeline Paiement
Camplani M.
+11 more
Chambers D.R.
Dima Damen
Dondi P.
Garća G.M.
Grenader E.
Lili Tao
Linder T.
Majid Mirmehdi
Massimo Camplani
Sion Hannuna
Tilo Burghardt
Publication date
1 January 2016
Publisher
'Institution of Engineering and Technology (IET)'
Doi
Cite
Abstract
© The Institution of Engineering and Technology. Multiple human tracking (MHT) is a fundamental task in many computer vision applications. Appearance-based approaches, primarily formulated on RGB data, are constrained and affected by problems arising from occlusions and/or illumination variations. In recent years, the arrival of cheap RGB-depth devices has led to many new approaches to MHT, and many of these integrate colour and depth cues to improve each and every stage of the process. In this survey, the authors present the common processing pipeline of these methods and review their methodology based (a) on how they implement this pipeline and (b) on what role depth plays within each stage of it. They identify and introduce existing, publicly available, benchmark datasets and software resources that fuse colour and depth data for MHT. Finally, they present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets
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