High Performance Long Term Tracking With Meta Updater
Long term visual tracking has drawn increasing attention because it is much closer to practical applications than short term tracking. Most top ranked long term trackers adopt the offline trained Siamese architectures, thus,they cannot benefit from great progress of short term trackers with online update. However, it is quite risky to straightforwardly introduce online update based trackers to solve the long term problem, due to long term uncertain and noisy observations. In this work, we propose a novel offline trained Meta Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame? The proposed meta updater can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module. Our meta updater learns a binary output to guide the tracker's update and can be easily embedded into different trackers. This work also introduces a long term tracking framework consisting of an online local tracker, an online verifier, a SiamRPN based re detector, and our meta updater. Numerous experimental results on the VOT2018LT,VOT2019LT, OxUvALT, TLP, and LaSOT benchmarks show that our tracker performs remarkably better than other competing algorithms. Our project is available on the website: https:/github.com/Daikenan/LTMU.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)