Fast and accurate gesture recognition based on motion shapes

Abstract

As in many other computer vision applications, the large amount of data is an inherent problem in video gesture recognition. A challenging task is to maintain a suitable trade-off between time and accuracy aiming a solution meeting certain requirements and constraints. In this paper, we propose a simple and fast gesture recognition approach that extracts meaningful and discriminative descriptors from hand gesture videos. Experiments conducted on the Sheffield Kinect Gestures (SKIG) data set show that our method achieves competitive accuracies, while processing frames at frequencies higher than those required for real-time applications.