What I Built
I built Laser-Guided Human Tracking, a Python-based computer vision project that explores face detection and tracking using computer vision tools.
The project uses OpenCV/CVZone and includes YOLOv8n model weights. It contains separate scripts for face detection and face tracking, providing a starting point for experimenting with real-time visual tracking.
My goal with this project was to explore how computer vision can detect and track people in a video stream and how software can interact with external hardware.
Demo
GitHub repository: https://github.com/ikshita22/laser-guided-tracker
Code
The main files are:
facedetection.py — face detection.
facetracking.py — face tracking.
yolov8n.pt — included YOLOv8n model weights.
README.md — project setup and dependencies.
How I Built It
I used Python and computer vision libraries to experiment with face detection and tracking. The repository includes CVZone, OpenCV-related dependencies, PyFirmata, and YOLOv8n weights.
The project explores how visual input can be processed to detect a person and follow their position. Its modular scripts make it possible to experiment with detection and tracking separately.
Why Does Open Innovation Matter?
Open-source computer vision tools make experimentation more accessible. Developers can inspect the implementation, modify the code, learn from existing models, and adapt the system to their own research and educational projects.
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