[Project
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]
Guangze Zheng¹, Shijie Lin¹, Haobo Zuo¹, Changhong Fu², Jia Pan¹*
PyTorch implementation for NetTrack. SOTA performance on BFT, TAO, TAO-OW, AnmimalTrack, and GMOT-40 without any training or finetuning!
Prerequisite
conda create -n nettrack python=3.10 # please use the default version
pip3 install torch torchvision # --index-url https://download.pytorch.org/whl/cu121
pip3 install -r requirements.txt
pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
pip3 install cython_bbox
sudo apt update
sudo apt install ffmpeg
Install Grounding DINO and CoTracker:
pip install git+https://github.com/IDEA-Research/GroundingDINO.git
pip install git+https://github.com/facebookresearch/co-tracker.git@8d364031971f6b3efec945dd15c468a183e58212
Prepare weights: Download the default pretrained Grouding DINO and CoTracker model:
cd weights
cd groundingdino
wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth
cd ..
mkdir cotracker && cd cotracker
wget https://dl.fbaipublicfiles.com/cotracker/cotracker_stride_4_wind_8.pth
cd ..
📊 Bird flock tracking (BFT) dataset:
📥 Download BFT dataset v1.5
Due to policy limitations of Alipan, please run the .exe file directly to decompress data.
Run default demo video.
sh tools/demo/demo_seq.sh
The results will be shown in ./output/track_res
.
Evaluate
Please ref to ./docs/evalutate.md
.
Watch our video on YouTube!
The primary data of BFT dataset is from the BBC nature documentary series Earthflight. The code is based on GroundingDINO, CoTracker, and ByteTrack. Dr. Ming-Shan Wang provided valuable biological suggestions for this work. The authors appreciate the great work and the contributions they made.
If you find this dataset useful, please cite our work. Looking forward to your suggestions to make this dataset better!
@Inproceedings{nettrack,
title={{NetTrack: Tracking Highly Dynamic Objects with a Net}},
author={Zheng, Guangze and Lin, Shijie and Zuo, Haobo and Fu, Changhong and Pan, Jia},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024},
pages={1-8}}