SeatSense

MISSION BRIEF

Developed a real-time library occupancy tracker using YOLOv8 computer vision to detect available study spaces across rooms, achieving 94% detection accuracy and demonstrating improved space utilization in 24 hours with a team of 4. Engineered a full-stack prototype integrating Python-based AI inference on Jetson hardware, a Node.js backend, and a React WebSocket-powered dashboard achieving 100ms frame refresh rates for near real-time monitoring.