Abstract
Gesture recognition systems have changed a lot recently, due to the development of modern data capture devices (sensors) and the development of new recognition algorithms. The article presents the results of a study for recognizing static and dynamic hand gestures from a video stream from RGB and RGBD cameras, namely from the Logitech HD Pro Webcam C920 webcam and from the Intel RealSense D435 depth camera. Software implementation is done using Python 3.6 tools. Open source Python libraries provide robust implementations of image processing and segmentation algorithms. The feature extraction and gesture classification subsystem is based on the VGG-16 neural network architecture implemented using the TensorFlow and Keras deep learning frameworks. The technical characteristics of the cameras are given. The algorithm of the application is described. The research results aimed at comparing data capture devices under various experimental conditions (distance and illumination) are presented. Experimental results show that using the Intel RealSense D435 depth camera provides more accurate gesture recognition under various experimental conditions.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
