Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID

This repository provides a strong baseline for multi-UAV tracking in thermal infrared videos by leveraging YOLOv12 and BoT-SORT with ReID. Our method provides a significant boost over the well-established YOLOv5 with the DeepSORT combination, offering a high-performance starting point for UAV swarm tracking.

📹 Preview:

strong_baseline.webm

🔗 Full video available at: Track 3

🔍 See also SOT inferences: Track 1 and Track 2

🌐 CVPR2025 | Workshops | 4th Anti-UAV Workshop | Track-1 | Track-2 | Track-3

🗞️ News

• April 13, 2025: The datasets presented in Table 2 of the paper are now available.
• April 7, 2025: Our paper is now on arXiv.
  • 🎥 Demos: Hugging Face | YouTube
  • 🚀 Quickstart: Colab Notebook | Kaggle Notebook
  • 🌐 Project Page: Link

🚀 Quickstart: Installation and Demonstration

Installation

$ conda create -n yolov12_botsort python=3.11 -y
$ conda activate yolov12_botsort
$ git clone https://github.com/wish44165/YOLOv12-BoT-SORT-ReID.git
$ cd YOLOv12-BoT-SORT-ReID/BoT-SORT/yolov12/
$ wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
$ pip install -r requirements.txt
$ pip install -e .
$ cd ../
$ pip3 install torch torchvision torchaudio
$ pip3 install -r requirements.txt
$ python3 setup.py develop
$ pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
$ pip3 install cython_bbox
$ pip3 install faiss-cpu

Folder Structure

YOLOv12-BoT-SORT-ReID/
├── data/
│   └── demo/
│       ├── MOT/
│       │   ├── Test_imgs/
│       │   │   ├── MultiUAV-003/
│       │   │   ├── MultiUAV-135/
│       │   │   ├── MultiUAV-173/
│       │   │   └── MultiUAV-261/
│       │   └── TestLabels_FirstFrameOnly/
│       │       ├── MultiUAV-003.txt
│       │       ├── MultiUAV-135.txt
│       │       ├── MultiUAV-173.txt
│       │       └── MultiUAV-261.txt
│       └── SOT/
│           ├── Track1/
│           │   ├── 20190926_111509_1_8/
│           │   ├── 41_1/
│           │   ├── new30_train-new/
│           │   └── wg2022_ir_050_split_01/
│           └── Track2/
│               ├── 02_6319_0000-1499/
│               ├── 3700000000002_110743_1/
│               ├── DJI_0057_1/
│               └── wg2022_ir_032_split_04/
└── BoT-SORT/

Demonstration

$ cd BoT-SORT/

# Track 1
$ python3 tools/predict_track1.py --weights ./yolov12/weights/SOT_yolov12l.pt --source ../data/demo/SOT/Track1/ --img-size 640 --device "0" --conf-thres 0.01 --iou-thres 0.01 --track_high_thresh 0.1 --track_low_thresh 0.01 --fuse-score --agnostic-nms --min_box_area 4 --save_path_answer ./submit/track1/demo --hide-labels-name
# output: ./runs/detect/, ./submit/track1/demo/

# Track 2
$ python3 tools/predict_track2.py --weights ./yolov12/weights/SOT_yolov12l.pt --source ../data/demo/SOT/Track2/ --img-size 640 --device "0" --conf-thres 0.01 --iou-thres 0.01 --track_high_thresh 0.1 --track_low_thresh 0.01 --fuse-score --agnostic-nms --min_box_area 1 --save_path_answer ./submit/track2/demo --hide-labels-name
# output: ./runs/detect/, ./submit/track2/demo/

# Track 3
$ python3 tools/predict_track3.py --weights ./yolov12/weights/MOT_yolov12n.pt --source ../data/demo/MOT/ --img-size 1600 --device "0" --track_buffer 60 --save_path_answer ./submit/track3/demo --hide-labels-name
# output: ./runs/detect/, ./submit/track3/demo/

# Heatmap
$ cd yolov12/
$ python heatmap.py
# output: ./outputs/

🛠️ Implementation Details

Hardware Information

Laptop

• CPU: Intel® Core™ i7-12650H
• GPU: NVIDIA GeForce RTX 4050 Laptop GPU (6GB)
• RAM: 23734MiB

HPC

• GPU: Spartan gpu-h100 (80GB), gpu-a100 (80GB)

🖻 Data Preparation

Officially Released

4th_Anti-UAV_Challenge/
├── baseline/
│   ├── Baseline_code.zip
│   └── MultiUAV_Baseline_code_and_submissi.zip
├── test/
│   ├── MultiUAV_Test.zip
│   ├── track1_test.zip
│   └── track2_test.zip
└── train/
    ├── MultiUAV_Train.zip
    └── train.zip

Strong Baseline

train/
├── MOT/
│   └── AntiUAV_train_val.zip
├── ReID/
│   ├── MOT20_subset.zip
│   └── MOT20.zip
└── SOT/
    ├── AntiUAV_train_val_test.zip
    └── AntiUAV_train_val.zip

Enhancement

Details soon

📂 Folder Structure

Follow the folder structure below to ensure smooth execution and easy navigation.

YOLOv12-BoT-SORT-ReID/
├── BoT-SORT/
│   ├── datasets/
│   │   └── README.md
│   ├── fast_reid/
│   │   └── datasets/
│   │       ├── generate_mot_patches.py
│   │       └── README.md
│   ├── logs/
│   │   └── README.md
│   ├── requirements.txt
│   ├── runs/
│   │   └── README.md
│   ├── submit/
│   │   └── README.md
│   ├── tools/
│   │   ├── predict_track1.py
│   │   ├── predict_track2.py
│   │   └── predict_track3.py
│   └── yolov12/
│       ├── heatmap.py
│       ├── imgs_dir/
│       │   ├── 00096.jpg
│       │   ├── 00379.jpg
│       │   ├── 00589.jpg
│       │   └── 00643.jpg
│       ├── requirements.txt
│       └── weights/
│           ├── MOT_yolov12n.pt
│           └── SOT_yolov12l.pt
├── data/
│   ├── demo/
│   ├── MOT/
│   │   └── README.md
│   └── SOT/
│       └── README.md
├── LICENSE
└── README.md

🔨 Reproduction

Details soon

✨ Models

Model	size (pixels)	mAPval 50-95	params (M)	FLOPs (G)
SOT_yolov12l.pt	640	67.2	26.3	88.5
MOT_yolov12n.pt	1600	77.7	2.6	6.3

📜 Citation

If you find this project helpful for your research or applications, we would appreciate it if you could give it a star and cite the paper.

@article{chen2025strong,
  title={Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID},
  author={Chen, Yu-Hsi},
  journal={arXiv preprint arXiv:2503.17237},
  year={2025}
}

🙏 Acknowledgments

A large portion of the code is adapted from YOLOv12 and BoT-SORT. Thanks for their excellent work!