3D Gesture Recognition Method Based on Faster R-CNN Network

The traditional static gesture recognition algorithm is easily affected by the complex environment which can cause low recognition rate. Therefore, an improved Faster R-CNN network is adopted to improve the gesture recognition accuracy based on 3D TOF (Time of Flight) camera. The contribution of this paper includes: 1) The group own TOF camera is used for data acquisition of 300 images for each of the five gestures, and the images collected by the TOF camera can reduce the effect of complex environments and provide depth information of gestures; 2) The gesture classification is improved by adding the Inception v3 in the Faster R-CNN. Experiments are carried out to show that the improved network has higher accuracy and stronger robustness, compared with traditional Faster R-CNN, and the localization of gestures in space is accurate with a small number of datasets.