Touchless Operations Using Hand Gestures Detection

Humans have only recently begun using hand gestures to interact with computers. The integration of the real and digital worlds is the aim of gesture recognition. It is considerably simpler to convey our intentions and ideas to the computer via hand gestures. A simple and efficient touchless method of interacting with computer systems is through hand gestures. However, the limited end-user adoption of hand gesture-based systems is mostly caused by the significant technical challenges involved in successfully identifying in-air movements. Image recognition is one of the many ways that a computer may identify a hand gesture. The ability to recognise human movements is made possible by the deployment of a convolutional neural network (CNN). In this research, we build a straightforward hand tracking technique to operate a Robot Operating System (ROS) based surveillance car with socket programming using Google MediaPipe, a Machine Learning (ML) pipeline that integrates Palm Detection and Hand Landmark Models. In the investigation, steering speed and direction of a ROS automobile are controlled. Vehicles for surveillance that can be operated using hand gestures may help to enhance security measures