Multi-stage analysis for long-term tracking

Abstract

In long-term visual object tracking, the tracking model would be prone to drift or corruption and the tracker can hardly catch the target again after tracking failures. A set of novel strategies for long-term tracking is proposed to solve these problems. First, a simple and efficient method is proposed to calculate the tracking confidence of Staple, a well-known tracker based on correlation filers. A model update mechanism is then developed to prevent model corruption. Furthermore, an online Support Vector Machine (SVM) classifier is trained to re-detect the object in case of unreliable tracking result. By means of intermittent sampling in the re-detection stage, the computational efficiency and the re-detection reliability are greatly improved. The combination of these new components in multi-stages spawns a real-time, accurate and robust tracker for long-term video. Experimental results demonstrate that our tracker, operating at a speed of 30 FPS, performs superiorly against some competitive trackers on robustness and accuracy, especially when the target encounters occlusion, severe deformation and out-of-view.