Hand posture recognition and tracking based on Bag-of-Words for human robot interaction

Abstract

Hand posture is a natural and effective interaction between human and robot. In this paper, we use monocular camera as input device, and an improved Bag-of-Words (BoW) method is proposed to detect and recognize hand posture based on a new descriptor ARPD (Appearance and Relative Position Descriptor) and spectral embedding clustering algorithm. To track hand motion rapidly and accurately, we have designed a new framework based on improved BoW and CAMSHIFT algorithm. The thorough evaluation of our algorithm is presented to show its usefulness.