PySOT is a software system designed by SenseTime Video Intelligence Research team. It implements state-of-the-art single object tracking algorithms, including SiamRPN and SiamMask. It is written in Python and powered by the PyTorch deep learning framework. This project also contains a Python port of toolkit for evaluating trackers.
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:
using the following backbone network architectures:
Additional backbone architectures may be easily implemented. For more details about these models, please see References below.
Evaluation toolkit can support the following datasets:
Model Zoo and Baselines
We provide a large set of baseline results and trained models available for download in the PySOT Model Zoo.
Please find installation instructions for PyTorch and PySOT in
Quick Start: Using PySOT
Add PySOT to your PYTHONPATH
Download models in PySOT Model Zoo and put the model.pth in the correct directory in experiments
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
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/)
assume still in experiments/siamrpn_r50_l234_dwxcorr
python ../../tools/eval.py \ --tracker_path ./results \ # result path --dataset VOT2018 \ # dataset name --num 1 \ # number thread to eval --tracker_prefix 'model' # tracker_name
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.
PySOT is released under the Apache 2.0 license.