Transfer learning-based discriminative correlation filter for visual tracking

Most Correlation Filter (CF)-based tracking methods can hardly handle occlusion or severe deformation, due to the lack of effective utilization of previous target information. To overcome this, we propose a novel Transfer Learning-based Discriminative Correlation Filter (TLDCF), which extracts knowledge from multiple previous tracking tasks and applies the knowledge for a new tracking task through Instance-Transfer Learning (ITL) and Probability-Transfer Learning (PTL). ITL applies knowledge of Gaussian Mixture Modelling (GMM) target representations and multi-channel filters learned in previous frames to directly train a new correlation filter. This improves the robustness of tracker for heavy occlusion and large appearance variations. Meanwhile, PTL encodes the spatio-temporal relationship predicted by Kalman Filter (KF) into a shared Gaussian prior to suppress huge location drift caused by similar targets. For optimization, we develop an efficient Alternating Direction Method of Multipliers (ADMM) based algorithm to calculate CFs on each independent channel in real time. Extensive experiments on OTB-2013 and OTB-2015 datasets well demonstrate the effectiveness of the proposed method. In particular, our method improves AUC score of the two datasets by 5.5% and 3.9% respectively compared to baseline, and achieves competitive performance against recent state-of-the-art deep trackers. (C) 2019 The Author(s). Published by Elsevier Ltd.