Multiple human tracking in RGB-depth data: a survey

Massimo Camplani, Adeline Paiement, Majid Mirmehdi, Dima Damen, Sion Hannuna, Tilo Burghardt, Lili Tao

Research output: Contribution to journalArticle (Academic Journal)

16 Citations (Scopus)
524 Downloads (Pure)

Abstract

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 (RGB-D) devices has {led} to many new approaches to MHT, and many of these integrate color and depth cues to improve each and every stage of the process. In this survey, we 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. We identify and introduce existing, publicly available, benchmark datasets and software resources that fuse color and depth data for MHT. Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.
Original languageEnglish
Pages (from-to)265-285
Number of pages21
JournalIET Computer VIsion
Volume11
Issue number4
Early online date12 Dec 2016
DOIs
Publication statusPublished - Jun 2017

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Structured keywords

  • Digital Health

Keywords

  • Human tracking
  • active and passive sensors
  • fusion of color and depth
  • Digital Health

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