Thermovision Based Cursor Control Using Infrared Imaging and Deep Learning

Touchless human-computer interaction is gaining significant attention for enhancing accessibility and hygiene. This project presents a cursor control system using thermovision, integrating deep learning with infrared imaging to enable screen navigation via hand gestures. The framework consists of four core components: a real-time thermal image processing pipeline that tracks the hottest regions via contour analysis and adaptive thresholding; a gesture classifier built on a TensorFlow Lite model, trained on thermal data to recognize five static gestures (FIST, ONE, PALM, SUPER, OPEN); a cursor engine that maps tracked hand movements to on-screen coordinates; and a stabilization module employing exponential moving averages and majority voting for improved accuracy and smoothness. By leveraging thermal tracking with a lightweight neural network, the system robustly handles varying lighting conditions, a common limitation of conventional RGB-based gesture systems. This thermographic approach provides reliable, contactless interaction without requiring visible light, making it highly suitable for assistive technology, industrial automation, and other touch-free applications. The entire system operates in real-time with low latency and is readily adaptable for edge device deployment.