Discriminative Mean Shift Tracking with Auxiliary Particles

We present a new approach towards efficient and robust tracking by incorporating the efficiency of the mean shift algorithm with the robustness of the particle filtering. The mean shift tracking algorithm is robust and effective when the representation of a target is sufficiently discriminative, the target does not jump beyond the bandwidth, and no serious distractions exist. In case of sudden motion, the particle filtering outperforms the mean shift algorithm at the expense of using a large particle set. In our approach, the mean shift algorithm is used as long as it provides reasonable performance. Auxiliary particles are introduced to conquer the distraction and sudden motion problems when such threats are detected. Moreover, discriminative features are selected according to the separation of the foreground and background distributions. We demonstrate the performance of our approach by comparing it with other trackers on challenging image sequences.