Online parallel framework for real-time visual tracking

Discriminative correlation filter (DCF) based tracking algorithms have recently achieved excellent performance in challenging factors such as rotations and distractors. However, in case with fast motions and full occlusion, these trackers are not always effective and easily drift. In contrast, Siamese matching networks are insensitive to fast motions, but suffer from distractors. In this work, we propose an efficient real-time parallel framework for robust visual tracking. By incorporating DCF and Siamese network into the tracking framework to complement each other and better distinguish target objects from background clutters. Additionally, to improve tracking results and prevent target drift, we introduce an effective tracker switch method to select suitable tracker in each frame by considering the confidence estimation as well as motion smoothness, and examines the impact among all trackers over time. To validate the effectiveness of our method, we perform comprehensive experiments on three popular benchmark datasets, namely OTB2013, OTB2015, VOT2016, and TC128. The experimental results demonstrate that the proposed algorithm exhibits favorably results and real-time tracking speed against state-of-art trackers.