Multiple human tracking in RGB-depth data: A survey

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

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.

• 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
• Contract Date: Apr 10, 2018