Real-Time Hand Gesture Recognition for Media Player using CNNs

A promising method for hand gesture identification is thermal imaging, which offers more robustness than conventional RGB camera-based systems in demanding settings. Thermal imaging provides resilience in a variety of settings, especially in different lighting circumstances, in contrast to RGB camera-based solutions. The study examines cutting-edge instruments and methods, such as real-time gesture mapping, CNN-based feature extraction, and effective model training approaches. The potential of thermal imaging to achieve accurate and user- friendly control over media functions including play, pause, and volume adjustments is highlighted by key discoveries. For smooth operation on devices with limited computational resources, the research focuses on resource-efficient architectures. This work lays the groundwork for creating gesture-controlled systems that are flexible, dependable, and easy to use.