Online learning 3D context for robust visual tracking

In this paper, we study the challenging problem of tracking single object in a complex dynamic scene. In contrast to most existing trackers which only exploit 2D color or gray images to learn the appearance model of the tracked object online, we take a different approach, inspired by the increased popularity of depth sensors, by putting more emphasis on the 3D Context to prevent model drift and handle occlusion. Specifically, we propose a 3D context-based object tracking method that learns a set of 3D context key-points, which have spatial–temporal co-occurrence correlations with the tracked object, for collaborative tracking in binocular video data. We first learn 3D context key-points via the spatial–temporal constrain in their spatial and depth coordinates. Then, the position of the object of interest is determined by a probability voting from the learnt 3D context key-points. Moreover, with depth information, a simple yet effective occlusion handling scheme is proposed to detect occlusion and recovery. Qualitative and quantitative experimental results on challenging video sequences demonstrate the robustness of the proposed method.