  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.
