A Feature-Based Object Tracking Method Using Online Template Switching and Feature Adaptation

This paper proposes a robust tracking method which stores representative object appearances as candidate templates during tracking, and selects the best template to match new frames. This online template adding and switching strategy, in one aspect, keeps update with new object appearances. In yet another aspect, it is resilient to misaligned templates and alleviates the drifting problem. The method is able to achieve real time speed on a laptop PC, and the tracking is robust to significant image variations including illumination changes, deformation, pose changes and occlusions.