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Being intensively studied, visual tracking has seen great recent advances ineither speed (e.g., with correlation filters) or accuracy (e.g., with deepfeatures). Real-time and high accuracy tracking algorithms, however, remainscarce. In this paper we study the problem from a new perspective and present anovel parallel tracking and verifying (PTAV) framework, by taking advantage ofthe ubiquity of multi-thread techniques and borrowing from the success ofparallel tracking and mapping in visual SLAM. Our PTAV framework typicallyconsists of two components, a tracker T and a verifier V, working in parallelon two separate threads. The tracker T aims to provide a super real-timetracking inference and is expected to perform well most of the time; bycontrast, the verifier V checks the tracking results and corrects T whenneeded. The key innovation is that, V does not work on every frame but onlyupon the requests from T; on the other end, T may adjust the tracking accordingto the feedback from V. With such collaboration, PTAV enjoys both the highefficiency provided by T and the strong discriminative power by V. In ourextensive experiments on popular benchmarks including OTB2013, OTB2015, TC128and UAV20L, PTAV achieves the best tracking accuracy among all real-timetrackers, and in fact performs even better than many deep learning basedsolutions. Moreover, as a general framework, PTAV is very flexible and hasgreat rooms for improvement and generalization.

Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking