Robust multiple object tracking in RGB-D camera networks

This paper presents a fast and robust multiple object tracking algorithm based on an RGB-D version of the MeanShift tracking algorithm and exploiting RGB-D camera networks when multiple RGB-D sensors are available. The original Mean-Shift algorithm has been improved in three ways. First, a color-depth Joint Probability Density Function is proposed for taking into account both depth and color information. Secondly, we propose an occlusion detection mechanism which can handle long-term occlusions even when objects move fast and unpredictably. Finally, when multiple views are available, we combine the tracking outcomes from all the RGB-D sensors in our network to deal with the identity confusion problem and enhance the overall tracking performance. Experimental results demonstrate that the proposed scheme is robust, realtime and has yielded a marked improvement with respect to the state-of-the-art in terms of tracking quality. As a further contribution, we released our work as open-source in order to provide the best benefit to the wide Computer Vision and Robotics community.