PySOT

PySOT 是商汤视频智能研究团队设计的软件系统。它实现了最先进的目标跟踪算法, 包括SiamRPN 和SiamMask. 它是用Python语言和 PyTorch 深度学习框架编写的. 此项目还包含用于评估跟踪器的python工具包.

PySOT 以及支持了以下研究项目, 包括: SiamRPN, DaSiamRPN, SiamRPN++, 和SiamMask.

Example SiamFC, SiamRPN and SiamMask outputs.

Introduction

The goal of PySOT is to provide a high-quality, high-performance codebase for visual tracking research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research. PySOT includes implementations of the following visual tracking algorithms:

• SiamMask
• SiamRPN++
• DaSiamRPN
• SiamRPN
• SiamFC

using the following backbone network architectures:

• ResNet{18, 34, 50}
• MobileNetV2
• AlexNet

Additional backbone architectures may be easily implemented. For more details about these models, please see References below.

Evaluation toolkit can support the following datasets:

📎 OTB2015 📎 VOT16/18/19 📎 VOT18-LT 📎 LaSOT 📎 UAV123

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the PySOT Model Zoo.

Installation

Please find installation instructions for PyTorch and PySOT in INSTALL.md.

Quick Start: Using PySOT

Add PySOT to your PYTHONPATH

export PYTHONPATH=/path/to/pysot:$PYTHONPATH

Download models

Download models in PySOT Model Zoo and put the model.pth in the correct directory in experiments

Webcam demo

python tools/demo.py \
    --config experiments/siamrpn_r50_l234_dwxcorr/config.yaml \
    --snapshot experiments/siamrpn_r50_l234_dwxcorr/model.pth
    # --video demo/bag.avi # (in case you don't have webcam)

Download testing datasets

Download datasets and put them into testing_dataset directory. Jsons of commonly used datasets can be downloaded from Google Drive or BaiduYun. If you want to test tracker on new dataset, please refer to pysot-toolkit to setting testing_dataset.

Test tracker

cd experiments/siamrpn_r50_l234_dwxcorr
python -u ../../tools/test.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file

The testing results will in the current directory(results/dataset/model_name/)

Eval tracker

assume still in experiments/siamrpn_r50_l234_dwxcorr_8gpu

python ../../tools/eval.py 	 \
	--tracker_path ./results \ # result path
	--dataset VOT2018        \ # dataset name
	--num 1 		 \ # number thread to eval
	--tracker_prefix 'model'   # tracker_name

Training 🔧

See TRAIN.md for detailed instruction.

Getting Help 🔨

If you meet problem, try searching our GitHub issues first. We intend the issues page to be a forum in which the community collectively troubleshoots problems.

• ModuleNotFoundError: No module named 'pysot'

🎯Solution: Run export PYTHONPATH=path/to/pysot first before you run the code.

• ImportError: cannot import name region

🎯Solution: Build region by python setup.py build_ext —-inplace as decribled in INSTALL.md.

References

• Fast Online Object Tracking and Segmentation: A Unifying Approach. Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H.S. Torr. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

• SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks. Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.

• Distractor-aware Siamese Networks for Visual Object Tracking. Zheng Zhu, Qiang Wang, Bo Li, Wu Wei, Junjie Yan, Weiming Hu. The European Conference on Computer Vision (ECCV), 2018.

• High Performance Visual Tracking with Siamese Region Proposal Network. Bo Li, Wei Wu, Zheng Zhu, Junjie Yan, Xiaolin Hu. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

• Fully-Convolutional Siamese Networks for Object Tracking. Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr. The European Conference on Computer Vision (ECCV) Workshops, 2016.

Contributors

• Fangyi Zhang
• Qiang Wang
• Bo Li

License

PySOT is released under the Apache 2.0 license.