Gesture Detection Using an Infrared Camera

Gestures and body language exert a significant impact on communication and interactions among individuals. This project's objective entailed the development of a gesture recognition system that analyzes the way people interact with their surroundings. The careful selection of the five recognized gestures ensures that the system remains focused on key limb placements that may dictate how one is perceived by the surrounding public. As the infrared (IR) camera captures real-time data, the system's ability to operate in low-light conditions further adds to its practicality and versatility in various environments. The convolutional neural network (CNN) plays a central role in the system's accuracy and efficiency. Its intensive training on a diverse dataset of images allows it to discern the distinct visual patterns associated with each gesture. As a result, the algorithm's Mean Average Precision (mAP) of 71.84% attests to its proficiency in accurately recognizing and classifying gestures.