Robust visual tracking via discriminative sequential ranking

Visual tracking is a fundamental task in computer vision. Although many efforts have been made in the past decades, it is still challenging to handle the complex factors in real world tracking scenarios. Ranking methods have shown their power on different data analysis tasks. However, we can not directly utilize this technique on sequential data for tracking. This is because a single ranking model cannot simultaneously reveal both the spatial and the temporal information. In this paper, we propose a novel discriminative sequential ranking (DSR) method to build appearance model for robust visual tracking. Our method can successfully handle both spatial and temporal variations by the coupled ranking processes. Specifically, the spatial process provides a target probability to reflects the intrinsic structure of the object at current frame. Meanwhile, the temporal process provides a background probability (guided by the sequential information) to stably describe the background appearance, which makes our tracker robust for background clutter. Experimental evaluations on the benchmark database with 50 challenging videos confirm that our method outperforms many other state-of-the-art tracking algorithms.