Deep Learning of Appearance Models for Online Object Tracking.

This paper introduces a deep learning based approach for vision based single target tracking. We address this problem by proposing a network architecture which takes the input video frames and directly computes the tracking score for any candidate target location by estimating the probability distributions of the positive and negative examples. An online fine-tuning step is carried out at every frame to learn the appearance of the target. The tracker has been tested on the standard tracking benchmark and the results indicate that the proposed solution achieves state-of-the-art tracking results.