SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

Cheng-Yen Yang, Hsiang-Wei Huang, Wenhao Chai, Zhongyu Jiang, Jenq-Neng Hwang

Information Processing Lab, University of Washington

[Arxiv] [Project Page] [Raw Results]

This repository is the official implementation of SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

samurai_demo.mp4

Getting Started

SAMURAI Installation

SAM 2 needs to be installed first before use. The code requires python>=3.10, as well as torch>=2.3.1 and torchvision>=0.18.1. Please follow the instructions here to install both PyTorch and TorchVision dependencies. You can install the SAMURAI version of SAM 2 on a GPU machine using:

cd sam2
pip install -e .
pip install -e ".[notebooks]"

Please see INSTALL.md from the original SAM 2 repository for FAQs on potential issues and solutions.

Install other requirements:

pip install matplotlib==3.7 tikzplotlib jpeg4py opencv-python lmdb pandas scipy loguru

SAM 2.1 Checkpoint Download

cd checkpoints && \
./download_ckpts.sh && \
cd ..

Data Preparation

Please prepare the data in the following format:

data/LaSOT
├── airplane/
│   ├── airplane-1/
│   │   ├── full_occlusion.txt
│   │   ├── groundtruth.txt
│   │   ├── img
│   │   ├── nlp.txt
│   │   └── out_of_view.txt
│   ├── airplane-2/
│   ├── airplane-3/
│   ├── ...
├── basketball
├── bear
├── bicycle
...
├── training_set.txt
└── testing_set.txt

Main Inference

python scripts/main_inference.py 

Demo on Custom Video

To run the demo with your custom video or frame directory, use the following examples:

Note: The .txt file contains a single line with the bounding box of the first frame in x,y,w,h format.

Input is Video File

python scripts/demo.py --video_path <your_video.mp4> --txt_path <path_to_first_frame_bbox.txt>

Input is Frame Folder

# Only JPG images are supported
python scripts/demo.py --video_path <your_frame_directory> --txt_path <path_to_first_frame_bbox.txt>

Acknowledgment

SAMURAI is built on top of SAM 2 by Meta FAIR.

The VOT evaluation code is modifed from VOT Toolkit by Luka Čehovin Zajc.

Citation

Please consider citing our paper and the wonderful SAM 2 if you found our work interesting and useful.

@article{ravi2024sam2,
  title={SAM 2: Segment Anything in Images and Videos},
  author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
  journal={arXiv preprint arXiv:2408.00714},
  url={https://arxiv.org/abs/2408.00714},
  year={2024}
}

@misc{yang2024samurai,
      title={SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory}, 
      author={Cheng-Yen Yang and Hsiang-Wei Huang and Wenhao Chai and Zhongyu Jiang and Jenq-Neng Hwang},
      year={2024},
      eprint={2411.11922},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.11922}, 
}