A Method of Stable Long-Term Single Object Tracking

We propose a stable long-term tracking method to deal with visual tracking in multiple complex scenarios to solve the problem of frequent disappearance and reappearance of targets in long-term tracking. In our method, we do not blindly start the global tracker once the target disappears, but use it only when necessary and in reasonable scope with the assistance of localization module. In addition, we designed an FP-verifier based on feature pools to reevaluate the candidate bounding boxes given by our local tracker and global tracker to ensure that online learning local tracker can be updated stably. Our method outperforms the state-of-the-art results on the VOT2020LT challenge. In addition, the control experiments show that our FP-verifier is more effective than the RT-MDNet verifier used by the top three winners of VOT2020LT challenge.