LTMU

• High-Performance Long-Term Tracking with Meta-Updater(CVPR2020 Oral && Best Paper Nomination).

Introduction

Our Meta-updater can be easily embedded into other online-update algorithms(Dimp, ATOM, ECO, RT-MDNet...) to make their online-update more accurately in long-term tracking task. More info.

Experiments

+MU = Basetracker+Meta-Updater

+LTMU = Basetracker+Meta-Updater+Global Detection+Verifier+Bbox refine | Tracker | LaSOT(AUC) | VOT2020 LT(F) | VOT2018 LT(F) | TLP(AUC) |Link| |:-----------:|:----------------:|:----------------:|:----------------:|:----------------:|:----------------:| | RT-MDNet| 0.335 |0.338 |0.367 |0.276 |Paper/Code/Results| | RT-MDNet+MU| 0.354 |0.396 |0.407 |0.337 |Paper/Code/Results| | ATOM| 0.511 |0.497 |0.510 |0.399 |Paper/Code/Results| | ATOM+MU | 0.541 |0.620 |0.628 |0.473 |Paper/Code/Results| | DiMP| 0.578 |0.573 |0.587 |0.514 |Paper/Code/Results| | DiMP+MU | 0.602 |0.641 |0.649 |0.564 |Paper/Code/Results| | DiMP+LTMU | 0.605 |0.691 |- |0.572 |Paper/Code/Results| | PrDiMP| 0.612 |0.632 |0.631 |0.535 |Paper/Code/Results| | PrDiMP+MU | 0.615 |0.661 |0.675 |0.582 |Paper/Code/Results| | SuperDiMP| 0.646 |0.647 |0.667 |0.552 |Paper/Code/Results| | SuperDiMP+MU| 0.658 |0.704 |0.707 |0.595 |Paper/Code/Results| | D3S | 0.494 |0.437 |0.445 |0.390 |Paper/Code/Results| | D3S+MU | 0.520 |0.518 |0.534 |0.413 |Paper/Code/Results| | ECO | - |- |- |- |Paper/Code/Results| | ECO+MU | - |- |- |- |Paper/Code/Results| | MDNet | - |- |- |- |Paper/Code/Results| | MDNet+MU | - |- |- |- |Paper/Code/Results|

Paper link

• Google Drive
• Baidu Yun 提取码：kexg ## Citation Please cite the above publication if you use the code or compare with the LTMU tracker in your work. Bibtex entry:

  @inproceedings{Dai_2020_CVPR,
  author = {Kenan Dai, Yunhua Zhang, Dong Wang, Jianhua Li, Huchuan Lu, Xiaoyun Yang},
  title = {{High-Performance Long-Term Tracking with Meta-Updater},
  booktitle = {CVPR},
  year = {2020}
  }
  

  ## Results ### LTMU
• LaSOT
• OxUvALT
• TLP
• VOTLT(18&19)