Accurate Scale Estimation for Robust Visual Tracking

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search while operating at real-time. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation.