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https://github.com/MasterBin-IIAU/AlphaRefine
Official implementation for the CVPR2021 paper Alpha-Refine
https://github.com/MasterBin-IIAU/AlphaRefine
alpha-refine cvpr2021 object-tracking refinement-module vot2020 winner
Last synced: 6 days ago
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Official implementation for the CVPR2021 paper Alpha-Refine
- Host: GitHub
- URL: https://github.com/MasterBin-IIAU/AlphaRefine
- Owner: MasterBin-IIAU
- License: gpl-3.0
- Created: 2020-07-03T07:13:39.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-10-03T21:36:42.000Z (about 1 year ago)
- Last Synced: 2024-08-02T06:13:02.254Z (3 months ago)
- Topics: alpha-refine, cvpr2021, object-tracking, refinement-module, vot2020, winner
- Language: Python
- Homepage:
- Size: 13.3 MB
- Stars: 188
- Watchers: 10
- Forks: 30
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# Alpha-Refine
This is the official implementation of [Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
](https://arxiv.org/abs/2012.06815).
![Architecture](doc/asset/AR-Architecture.png)## News
- :warning: We provide a concise script [demo.py](demo.py) as an example of applying alpha refine to dimp.
**We recommend taking this script as the starting point of exploring our project**.
- A TensorRT optimized version of AlphaRefine is available [here](https://github.com/ymzis69/AlphaRefine_TensorRT).
- The code for **CVPR2021** is updated. The old version is still available by
git clone -b vot2020 https://github.com/MasterBin-IIAU/AlphaRefine.git
- AlphaRefine is accepted by the **CVPR2021**
- :trophy: **Alpha-Refine wins VOT2020 Real-Time Challenge with EAOMultistart 0.499!**
- VOT2020 winner presentation [slide](VOT20-RT-Report.pdf) has been uploaded.## Setup Alpha-Refine
* **Install AlphaRefine**
```bash
git clone https://github.com/MasterBin-IIAU/AlphaRefine.git
cd AlphaRefine
```
Run the installation script to install all the dependencies. You need to provide the `${conda_install_path}`
(e.g. `~/anaconda3`) and the name `${env_name}` for the created conda environment (e.g. `alpha`).
```
# install dependencies
bash install.sh ${conda_install_path} ${env_name}
conda activate alpha
python setup.py develop
```* **Download AlphaRefine Models**
We provide the models of *AlphaRefine* here. The **AUC** and **Latency** are tested with SiamRPN++ as the base tracker
on *LaSOT* dataset, using a RTX 2080Ti GPU.We recommend download the model into `ltr/checkpoints/ltr/SEx_beta`.
| Tracker | Backbone | Latency | AUC(%) | Model |
|:--------------:|:----------------:|:-----------:|:-----------:|:----------------:|
| AR34c+m | ResNet34 | 5.1ms | 55.9 | [google](https://drive.google.com/file/d/1drLqNq4r9g4ZqGtOGuuLCmHJDh20Fu1m/view?usp=sharing)/[baidu](https://pan.baidu.com/s/1ZCJKk1mXE_96BEpwGiEuMQ)[key:jl1m]|
| AR18c+m | ResNet18 | 4.2ms | 55.0 | [google](https://drive.google.com/file/d/1ANf0KCvlFBbGQPpvT-3WNiy414ANkgLZ/view?usp=sharing)/[baidu](https://pan.baidu.com/s/1IIaRNkFVPSG1s71g255CHw)[key:83ef]|When combined with more powerful base trackers, *AlphaRefine* leads to very competitive tracking systems (e.g. *ARDiMP*).
Following are some of the best performed trackers on LaSOT. Results are present in [Performance](#performance)* **Demo**
We provide a concise [demo.py](demo.py) as an example for applying alpha refine to dimp.
**We recommend you should take this script as the starting point of exploring our project**.
You may need [doc/Reproduce.md](doc/Reproduce.md) for setting up the base trackers of our experiments.## How to apply Alpha-Refine to Your Own Tracker
We provide a concise [demo.py](demo.py) as an example for applying alpha refine to dimp.## How to Train Alpha-Refine
Please refer to [doc/TRAIN.md](doc/TRAIN.md) for the guidance of training Alpha-Refine.After training, you can refer to [doc/Reproduce.md](doc/Reproduce.md) for reproducing our experiment result.
## Performance
When combined with more powerful base trackers,
*AlphaRefine* leads to very competitive tracking systems (e.g. *ARDiMP*).
For more performance reports, please refer to our [paper](https://arxiv.org/abs/2012.06815).
**You can refer to [doc/Reproduce.md](doc/Reproduce.md) for reproducing our result.*** **LaSOT**
| Tracker | Success Score | Speed (fps) | Paper/Code |
|:----------- |:----------------:|:----------------:|:----------------:|
| ARDiMP (ours) | 0.654 | 32 (RTX 2080Ti) | [Paper](https://arxiv.org/abs/2012.06815)/[Result](https://drive.google.com/file/d/1UNPwz7qP8SeBTxHF_Cw0JLmrN1jTqJJE/view?usp=sharing) |
| Siam R-CNN (CVPR20) | 0.648 | 5 (Tesla V100) | [Paper](https://arxiv.org/pdf/1911.12836.pdf)/[Code](https://github.com/VisualComputingInstitute/SiamR-CNN) |
| DimpSuper | 0.631 | 39 (RTX 2080Ti) | [Paper](https://arxiv.org/pdf/2003.12565.pdf)/[Code](https://github.com/visionml/pytracking) |
| ARDiMP50 (ours) | 0.602 | 46 (RTX 2080Ti) | [Paper](https://arxiv.org/abs/2012.06815)/[Result](https://drive.google.com/file/d/1wJc_-1lCxeGlqEAKd1qER1x_4bWAhujv/view?usp=sharing) |
| PrDiMP50 (CVPR20) | 0.598 | 30 (Unkown GPU) | [Paper](https://arxiv.org/pdf/2003.12565.pdf)/[Code](https://github.com/visionml/pytracking) |
| LTMU (CVPR20) | 0.572 | 13 (RTX 2080Ti) | [Paper](https://arxiv.org/abs/2004.00305)/[Code](https://github.com/Daikenan/LTMU) |
| DiMP50 (ICCV19) | 0.568 | 59 (RTX 2080Ti) | [Paper](https://arxiv.org/pdf/1904.07220.pdf)/[Code](https://github.com/visionml/pytracking) |
| Ocean (ECCV20) | 0.560 | 25 (Tesla V100) | [Paper](https://arxiv.org/abs/2006.10721)/[Code](https://github.com/researchmm/TracKit) |
| ARSiamRPN (ours) | 0.560 | 50 (RTX 2080Ti) | [Paper](https://arxiv.org/abs/2012.06815)/[Result](https://drive.google.com/file/d/1u-ou43O_RU9oRFx1UKjzeYe6e-4qnMZZ/view?usp=sharing) |
| SiamAttn (CVPR20) | 0.560 | 45 (RTX 2080Ti) | [Paper](https://arxiv.org/pdf/2004.06711.pdf)/[Code]() |
| SiamFC++GoogLeNet (AAAI20)| 0.544 | 90 (RTX 2080Ti) | [Paper](https://arxiv.org/pdf/1911.06188.pdf)/[Code](https://github.com/MegviiDetection/video_analyst) |
| MAML-FCOS (CVPR20) | 0.523 | 42 (NVIDIA P100) | [Paper](https://arxiv.org/pdf/2004.00830.pdf)/[Code]() |
| GlobalTrack (AAAI20) | 0.521 | 6 (GTX TitanX) | [Paper](https://arxiv.org/abs/1912.08531)/[Code](https://github.com/huanglianghua/GlobalTrack) |
| ATOM (CVPR19) | 0.515 | 30 (GTX 1080) | [Paper](https://arxiv.org/pdf/1811.07628.pdf)/[Code](https://github.com/visionml/pytracking) |## Acknowledgments
* This repo is based on [Pytracking](https://github.com/visionml/pytracking.git) which is an exellent work.
* Thanks for [pysot](https://github.com/STVIR/pysot) and [RTMDNet](https://github.com/IlchaeJung/RT-MDNet) from which
we borrow the code as base trackers.