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The current deep learning based visual tracking approaches have been verysuccessful by learning the target classification and/or estimation model from alarge amount of supervised training data in offline mode. However, most of themcan still fail in tracking objects due to some more challenging issues such asdense distractor objects, confusing background, motion blurs, and so on.Inspired by the human "visual tracking" capability which leverages motion cuesto distinguish the target from the background, we propose a Two-Stream ResidualConvolutional Network (TS-RCN) for visual tracking, which successfully exploitsboth appearance and motion features for model update. Our TS-RCN can beintegrated with existing deep learning based visual trackers. To furtherimprove the tracking performance, we adopt a "wider" residual network ResNeXtas its feature extraction backbone. To the best of our knowledge, TS-RCN is thefirst end-to-end trainable two-stream visual tracking system, which makes fulluse of both appearance and motion features of the target. We have extensivelyevaluated the TS-RCN on most widely used benchmark datasets including VOT2018,VOT2019, and GOT-10K. The experiment results have successfully demonstratedthat our two-stream model can greatly outperform the appearance based tracker,and it also achieves state-of-the-art performance. The tracking system can runat up to 38.1 FPS.

Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks