Real-time object tracking platform using SiamRPN (Siamese Region Proposal Network) for computer vision applications.

VisioTrack is a full-stack application that enables users to track objects in videos using deep learning. The system consists of:

• Frontend - Next.js web interface for video upload and visualization
• Backend - Python API with SiamRPN tracking model

VisioTrack/ ├── model/ # Python backend (API + ML model) │ ├── app.py # FastAPI server (HF Spaces) │ ├── colab_api.py # Flask server (Colab) │ ├── siamrpn.py # SiamRPN tracker │ ├── model.pth # Pre-trained weights │ ├── Dockerfile # Container config │ └── README.md # Backend documentation │ └── website/ # Next.js frontend ├── app/ # Pages and components ├── public/ # Static assets └── README.md # Frontend documentation 

• View pre-labeled training videos
• Analyze frame-by-frame annotations
• Understand tracking behavior

• Upload custom videos
• Draw bounding boxes on first frame
• Submit to backend for GPU processing
• Download tracked results

• Real-time object tracking
• GPU acceleration support
• Browser-compatible video encoding
• Interactive web interface
• REST API integration

• Next.js 16 (React 19)
• TypeScript
• Tailwind CSS v4
• Framer Motion

• Python 3.10+
• PyTorch
• FastAPI / Flask
• OpenCV
• FFmpeg

• Vercel (Frontend)
• Hugging Face Spaces (Backend)
• Google Colab (Backend - Free GPU)

• Frontend Setup: website/README.md
• Backend Setup: model/README.md
• API Reference: Available at /docs endpoint (FastAPI)

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

• SiamRPN algorithm and pre-trained model
• GOT-10k dataset
• Hugging Face Spaces platform
• Google Colab for free GPU access

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