Stereo Context for Robust Object Tracking

Context has been successfully applied to 2D image processing in the tasks of object detection, object recognition, etc. However, it is usually ignored in 3D object tracking. In this paper, we propose a stereo context-based object tracker that explores 3D context information for collaborative tracking in binocular video streams. The target is located by voting from companions, which are keypoints that have strong 3D motion correlations to the target. The companions are obtained by using spatial-temporal constraints in 3D space. Quantitative experimental results on challenging video sequences demonstrate the robustness and superior performance of the proposed tracker.