Skip to main content

Research Repository

Advanced Search

Multiple human tracking in RGB-depth data: A survey

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

Multiple human tracking in RGB-depth data: A survey Thumbnail


Authors

Massimo Camplani

Adeline Paiement

Majid Mirmehdi

Dima Damen

Sion Hannuna

Tilo Burghardt

Lili Tao



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.

Citation

Camplani, M., Paiement, A., Mirmehdi, M., Damen, D., Hannuna, S., Burghardt, T., & Tao, L. (2017). Multiple human tracking in RGB-depth data: A survey. IET Computer Vision, 11(4), 265-285. https://doi.org/10.1049/iet-cvi.2016.0178

Journal Article Type Review
Acceptance Date Nov 8, 2016
Publication Date Jun 1, 2017
Deposit Date Apr 10, 2018
Publicly Available Date Apr 10, 2018
Journal IET Computer Vision
Print ISSN 1751-9632
Electronic ISSN 1751-9640
Publisher Institution of Engineering and Technology (IET)
Peer Reviewed Peer Reviewed
Volume 11
Issue 4
Pages 265-285
DOI https://doi.org/10.1049/iet-cvi.2016.0178
Keywords object tracking, computer vision, image fusion, image colour analysis
Public URL https://uwe-repository.worktribe.com/output/904828
Publisher URL http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0178

Files







Downloadable Citations