Robust visual tracking based on adaptive depth-color-cue integration using range sensor

In visual tracking field, multi-cue integration has been researched extensively, but only color-based method still suffers from illumination changes, color-similar background or complete occlusion. To overcome these shortages, this paper presents an adaptive depth-color-cue integration framework for Mean-shift tracking. The state-of-art 2D rectangles evolves to 3D cubes for representing target region, and depth and color cues are combined together for representing target appearance. Moreover, a novel depth-data-based motion detection method is introduced to get more reliable motion cues during tracking. Furthermore, a reliability evaluation function is proposed to tune cues´ weights based on the assumption that most reliable cues are those which are most discriminative between target region and background regions. Finally, cues´ probability distribution maps are integrated for Mean-shift tracking. Extensive experiments under various conditions demonstrate the reliability and robustness of this depth-color-integrated tracking framework.