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Publication - Dr Sion Hannuna

    Multiple human tracking in RGB-depth data

    a survey

    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, vol 11., pp. 265-285

    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.

    Full details in the University publications repository