Python Real-Time Object Tracking with OpenCV
This project showcases a real-time object tracking system based on the Lucas-Kanade Optical Flow algorithm, implemented with OpenCV in Python. It is designed as a lightweight and modular framework that runs efficiently on both desktop and low-power embedded platforms.
The system allows interactive selection of the target object and provides visual feedback with dynamic overlays. It is optimized for scenarios requiring responsive tracking under varying video resolutions and frame rates. Development and testing are being conducted across multiple platforms including Windows, Linux (Ubuntu), and single-board computers.
Key Features:
- 🎯 Real-time tracking using Lucas-Kanade Optical Flow
- 🖱️ Interactive target selection via mouse
- 📐 Resolution-adaptive drawing and visual feedback
- 💻 Compatible with desktop and embedded platforms (e.g. Raspberry Pi)
- ⚙️ Built with Python, OpenCV, and NumPy
- 🔧 Structured for future algorithm integration
A lightweight, real-time object tracking system built with Python and OpenCV using the Lucas-Kanade Optical Flow algorithm. It supports interactive target selection and adaptive visual feedback. Designed to run efficiently on both desktop and embedded platforms such as Raspberry Pi, the system provides a modular foundation for future algorithm integration.