Long-term visual tracking based on adaptive correlation filters

Abstract

During the tracking, kernelized correlation filters may fail as the target is occluded seriously and goes out of view. To solve this problem, a long-term visual tracking algorithm based on adaptive correlation filters is proposed. First, we learn two correlation filters to locate the target and estimate the target scale, respectively. Meanwhile, we learn an independent target appearance correlation filter conservatively updated to know the occlusion degree of the target. Second, we combine the Kalman filter to predict and the support vector machine detector to redetect when tracking failure occurs, caused by the target undergoing severe occlusion or disappearing in the camera view. Third, to solve model drifts due to serious appearance changes of the target, we apply an adaptive model updating strategy to update the correlation filters and classifier. Extensive experimental results on the OTB2013 benchmark dataset demonstrate that our proposed method achieves the excellent overall performance against the nine state-of-the-art methods while running efficiently in real time.