https://github.com/foolwood/DaSiamRPN
[ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking
https://github.com/foolwood/DaSiamRPN
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[ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking
- Host: GitHub
- URL: https://github.com/foolwood/DaSiamRPN
- Owner: foolwood
- License: mit
- Created: 2018-08-13T11:14:59.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-01-08T09:29:54.000Z (almost 6 years ago)
- Last Synced: 2025-05-23T14:18:45.325Z (5 months ago)
- Language: Python
- Homepage:
- Size: 8.42 MB
- Stars: 1,275
- Watchers: 54
- Forks: 362
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
:trophy:News: **We won the VOT-18 real-time challenge**
:trophy:News: **We won the second place in the VOT-18 long-term challenge**
# DaSiamRPN
This repository includes PyTorch code for reproducing the results on VOT2018.
[**Distractor-aware Siamese Networks for Visual Object Tracking**](https://arxiv.org/pdf/1808.06048.pdf)
Zheng Zhu\*, Qiang Wang\*, Bo Li\*, Wei Wu, Junjie Yan, and Weiming Hu
*European Conference on Computer Vision (ECCV), 2018*
## Introduction
**SiamRPN** formulates the task of visual tracking as a task of localization and identification simultaneously, initially described in an [CVPR2018 spotlight paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_High_Performance_Visual_CVPR_2018_paper.pdf). (Slides at [CVPR 2018 Spotlight](https://drive.google.com/open?id=1OGIOUqANvYfZjRoQfpiDqhPQtOvPCpdq))
**DaSiamRPN** improves the performances of SiamRPN by (1) introducing an effective sampling strategy to control the imbalanced sample distribution, (2) designing a novel distractor-aware module to perform incremental learning, (3) making a long-term tracking extension. [ECCV2018](https://arxiv.org/pdf/1808.06048.pdf). (Slides at [VOT-18 Real-time challenge winners talk](https://drive.google.com/open?id=1dsEI2uYHDfELK0CW2xgv7R4QdCs6lwfr))
## Prerequisites
CPU: Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz
GPU: NVIDIA GTX1060
- python2.7
- pytorch == 0.3.1
- numpy
- opencv
## Pretrained model for SiamRPN
In our tracker, we use an AlexNet variant as our backbone, which is end-to-end trained for visual tracking.
The pretrained model can be downloaded from google drive: [SiamRPNBIG.model](https://drive.google.com/file/d/1-vNVZxfbIplXHrqMHiJJYWXYWsOIvGsf/view?usp=sharing).
Then, you should copy the pretrained model file `SiamRPNBIG.model` to the subfolder './code', so that the tracker can find and load the pretrained_model.
## Detailed steps to install the prerequisites
- install pytorch, numpy, opencv following the instructions in the `run_install.sh`. Please do **not** use conda to install.
- you can alternatively modify `/PATH/TO/CODE/FOLDER/` in `tracker_SiamRPN.m`
If the tracker is ready, you will see the tracking results. (EAO: 0.3827)
## Results
All results can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1HJOvl_irX3KFbtfj88_FVLtukMI1GTCR?usp=sharing).
| | VOT2015A / R / EAO | VOT2016A / R / EAO | VOT2017 & VOT2018A / R / EAO | OTB2015OP / DP | UAV123AUC / DP | UAV20LAUC / DP |
| :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| **SiamRPN** CVPR2017 | 0.58 / 1.13 / 0.349 | 0.56 / 0.26 / 0.344 | 0.49 / 0.46 / 0.244 | 81.9 / 85.0 | 0.527 / 0.748 | 0.454 / 0.617 |
| **DaSiamRPN** ECCV2018 | **0.63** / **0.66** / **0.446** | **0.61** / **0.22** / **0.411** | 0.56 / 0.34 / 0.326 | **86.5** / **88.0** | **0.586** / **0.796** | **0.617** / **0.838** |
| **DaSiamRPN** VOT2018 | - | - | **0.59** / **0.28** / **0.383** | - | - | - |
# Demo and Test on OTB2015
- To reproduce the reuslts on paper, the pretrained model can be downloaded from [Google Drive](https://drive.google.com/open?id=1BtIkp5pB6aqePQGlMb2_Z7bfPy6XEj6H): `SiamRPNOTB.model`.
:zap: :zap: This model is the **fastest** (~200fps) Siamese Tracker with AUC of 0.655 on OTB2015. :zap: :zap:
- You must download OTB2015 dataset (download [script](code/data/get_otb_data.sh)) at first.
A simple test example.
```
cd code
python demo.py
```
If you want to test the performance on OTB2015, please using the follwing command.
```
cd code
python test_otb.py
python eval_otb.py OTB2015 "Siam*" 0 1
```
# License
Licensed under an MIT license.
## Citing DaSiamRPN
If you find **DaSiamRPN** and **SiamRPN** useful in your research, please consider citing:
```
@inproceedings{Zhu_2018_ECCV,
title={Distractor-aware Siamese Networks for Visual Object Tracking},
author={Zhu, Zheng and Wang, Qiang and Bo, Li and Wu, Wei and Yan, Junjie and Hu, Weiming},
booktitle={European Conference on Computer Vision},
year={2018}
}
@InProceedings{Li_2018_CVPR,
title = {High Performance Visual Tracking With Siamese Region Proposal Network},
author = {Li, Bo and Yan, Junjie and Wu, Wei and Zhu, Zheng and Hu, Xiaolin},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018}
}
```