主催: 一般社団法人 日本機械学会
会議名: ロボティクス・メカトロニクス 講演会2017
開催日: 2017/05/10 - 2017/05/13
In this research, we present a hand shape estimation method from a pair of color and depth images obtained from a RGB-D camera during object manipulation where a hand and an object are mutually occluded. In the proposed method, a depth image is segmented into hand, object, and background regions, and two-stream convolutional neural networks (CNN) are trained to classify the hand shape given a pair of segmented depth images of the hand region and the object region. Experimental results show that a high recognition ratio can be achieved by the proposed method compared with four different one-stream CNNs trained by color images, unsegmented hand and object region depth images, hand region depth images, and object region depth images.