Social Distance Detection System

Project Details

An intelligent computer vision system designed to automatically monitor and enforce social distancing guidelines in real-time environments. The system leverages state-of-the-art deep learning models and advanced image processing techniques to detect people and calculate distances between them.

Technical Implementation

The project utilizes the pre-trained YOLO v5 (You Only Look Once) object detection model, specifically optimized for person detection. The system processes video frames in real-time, applies the detection algorithm, and then computes Euclidean distances between detected person bounding boxes. When the distance falls below the configured threshold (typically 6 feet or 2 meters), the system triggers visual alerts and highlights the violation areas.

Performance & Optimization

Optimized for real-time performance with frame rate optimization techniques, multi-threading for video processing, and efficient memory management. The system maintains high accuracy (95%+ person detection rate) while processing at 25-30 FPS on standard hardware configurations.