文档介绍：Mask-RCNN
王健，等：基于双通道 的手势识别
44 3 NG Ying
（School of Electronic and Information Engineering，Changchun University of Science and Technology，Changchun 130022）
Abstrat：Lighting，complex background and changeable gestures are always the difficulties of gesture recognition. With the
continuous development of acquisition equipment，the depth data collected by the depth camera adds more characteristic in-
formation to the color data，which effectively solves the problem of low recognition rate of gestures caused by lighting
changes. Therefore，in this paper，the advantages of deep learning and RGB-D data are combined，and a dual-channel
Mask RCNN network is proposed that takes RGB-D data as input. A deep feature extraction channel is added on the basis
of the original network；and the RGB features are fused with the depth features obtained after preprocessing on the feature
level. Finally，in order to avoid overfitting of the training model，a perturbation overlap rate algorithm is proposed to further
improve the recognition rate of gesture detection. The experimental results show that compared with the Mask RCNN net-
work using only color maps，the method in this paper improves the recognition rate on simple data sets by % and in-
creases on difficult data sets by