Robust object tracking with RGBD-based sparse learning

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.