🔹 Description

The Hand Gesture Detection system is a real-time computer vision application designed to identify and interpret hand gestures from video input. The system captures visual data through a camera feed and processes it using deep learning models to accurately detect hands and recognize predefined gestures.

Using YOLOv8 for object detection, the system locates hands within each video frame, while OpenCV handles image processing and real-time video streaming. The model is trained using PyTorch to ensure accurate gesture recognition under different lighting conditions and backgrounds.

This system can be used in various applications such as human-computer interaction, touchless controls, automation systems, and accessibility solutions. The project demonstrates practical experience in deep learning, real-time detection, and AI-based interaction systems.

🔹 Key Features

• Real-time hand detection and gesture recognition
• Deep learning-based object detection using YOLOv8
• Live video processing with OpenCV
• High accuracy across varying conditions
• Scalable for automation and HCI applications

🔹 Technologies Used

• Programming Language: Python
• Computer Vision: OpenCV
• Deep Learning Framework: PyTorch
• Object Detection Model: YOLOv8