Multiple human tracking in RGB-depth data: A survey

© 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

Multisubjects Tracking by Time-of-Flight Camera

Time-of-Flight cameras are the state of art sensors for a fast detection of depth data in a scene. This kind of sensors can be very useful for tracking, in particular in indoor ambient, since, using light in near-infrared spectrum, they are less affected by abrupt change in illumination. In this paper we propose a new method for the tracking of multiple subjects based on Kalman filter. The first step of our solution is a ToF based foreground segmentation, that retrieves all significant clusters in the scene, followed by a robust tracking system able to correctly handle occlusions and possible merging between clusters