Multiple Human Tracking in RGB-D Data: A Survey - Adeline Paiement (IET Computer Vision, 2016)
Multiple human tracking (MHT) is a fundamental task in many computer visionapplications. Appearance-based approaches, primarily formulated on RGB data,are constrained and affected by problems arising from occlusions and/orillumination variations. In recent years, the arrival of cheap RGB-Depth(RGB-D) devices has {led} to many new approaches to MHT, and many of theseintegrate 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 andreview 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 introduceexisting, publicly available, benchmark datasets and software resources thatfuse color and depth data for MHT. Finally, we present a brief comparativeevaluation of the performance of those works that have applied their methods tothese datasets.