This paper studies the appearance learning of object track- ing in challenging scenes. We propose a new appearance modeling ap- proach in the deep learning architecture for object tracking. Visual prior is learned from a large set of unlabeled images. Then it is transferred to the appearance model during tracking. Traditional trackers usually do tracking before updating at every input image. Drift may occur when there are complex appearance variations. We propose to update the ap- pearance model before tracking. This can effectively prevent tracking failures when there are complex appearance changes. And the motion parameters estimation could be more accurate with the updated appear- ance model. Experimental results on challenging videos demonstrate the robustness and accuracy of the proposed algorithm compared with sev- eral state of the art approaches.
['Jianwei Ding', 'Yunqi Tang', 'Huawei Tian', 'Yong-Zhen Huang']
Robust Appearance Learning for Object Tracking in Challenging Scenes