  Although there has been significant progress in the past decade,tracking is
still a very challenging computer vision task, due to problems such as
occlusion and model drift.Recently, the increased popularity of depth sensors
e.g. Microsoft Kinect has made it easy to obtain depth data at low cost.This
may be a game changer for tracking, since depth information can be used to
prevent model drift and handle occlusion.In this paper, we construct a
benchmark dataset of 100 RGBD videos with high diversity, including deformable
objects, various occlusion conditions and moving cameras. We propose a very
simple but strong baseline model for RGBD tracking, and present a quantitative
comparison of several state-of-the-art tracking algorithms.Experimental results
show that including depth information and reasoning about occlusion
significantly improves tracking performance. The datasets, evaluation details,
source code for the baseline algorithm, and instructions for submitting new
models will be made available online after acceptance.
