A stable long-term object tracking method with re-detection strategy

Abstract In this work, we proposed a long-term tracking strategy to deal with the occlusion, out-of-plane rotation, and the confusing non-target object. Our tracking system is composed of two parts, the CA-CF tracker, an efficient correlation method for short-term tracking, and the SVM-based re-detector, which prevents the CA tracker from degradation. When the tracker works with confidence, the CA-CF module ensures an accurate tracking result and the SVM updates accordingly. When the response maps fluctuate heavily, the SVM switches to work as a re-detector and the tracker will be initialized. We also introduced to adopt both the maximum response criterion and the APCE criterion to judge the performance of the tracker in time. By evaluating our algorithm on the OTB benchmark datasets, we proposed to analyze the result affected by the parameters of our CA-CF-SVM strategy. The experimental results show that our method has a significant improvement than the state-of-the-art methods for the long-term tracking both in accuracy and robustness.