SiamTrack

A Siamese network-based single object tracking framework modified from PySOT. This project provides a flexible and efficient visual object tracking implementation that supports various backbone networks and tracking strategies.

中文文档

Features

• Siamese network-based single object tracking framework
• Support for multiple backbone networks (ResNet, AlexNet, MobileNetV2, etc.)
• Complete training, testing, and evaluation tools
• Visualization demos and CAM (Class Activation Mapping) analysis tools
• Easily extensible modular design

Requirements

• Python 3.6+
• PyTorch 1.0+
• CUDA 9.0+ (recommended for GPU acceleration)
• Other dependencies (see installation instructions)

Installation

1. Clone the repository

git clone https://github.com/yourusername/siamtrack.git
cd siamtrack

2. Create and activate a virtual environment (optional but recommended)

conda create -n siamtrack python=3.7
conda activate siamtrack

3. Install dependencies

pip install -r requirements.txt

Project Structure

siamtrack/
├── siamese/           # Core tracking framework
│   ├── core/          # Configuration and core functionality
│   ├── datasets/      # Dataset interfaces
│   ├── models/        # Network model definitions
│   │   ├── backbone/  # Backbone networks (ResNet, AlexNet, etc.)
│   │   ├── head/      # Network heads
│   │   └── neck/      # Network necks
│   ├── tracker/       # Tracker implementations
│   └── utils/         # Utility functions
├── toolkit/           # Evaluation toolkit
├── tools/             # Training and testing tools
│   ├── train.py       # Training script
│   ├── test.py        # Testing script
│   ├── eval.py        # Evaluation script
│   └── demo.py        # Demo script
├── demo/              # Demo videos
├── experiments/       # Experiment configurations
├── pretrained_models/ # Pretrained models
└── CAM/               # Class Activation Mapping analysis

Usage

Training

Start training with the following command:

cd tools
python train.py --config ../experiments/config.yaml

Testing

Evaluate the model on test datasets:

cd tools
python test.py --config ../experiments/config.yaml --snapshot model.pth

Demo

Run a video object tracking demo using a pretrained model:

cd tools
python demo.py --config ../experiments/config.yaml --snapshot model.pth --video_name demo/video.mp4

Supported Models

• Backbone Networks:
  • ResNet (18, 34, 50)
  • AlexNet
  • MobileNetV2

Citation

If you use this project in your research, please consider citing:

@inproceedings{your-reference,
  title={Your Paper Title},
  author={Your Name},
  booktitle={Conference},
  year={Year}
}

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Acknowledgements

This project is modified based on PySOT. We thank the original authors for their contributions.