A Two-Stream Network For Driving Hand Gesture Recognition

Abstract

The number of traffic accident deaths caused by driving is increasing every year, in which the improper driving behaviors account for a large proportion of traffic accidents. To alert the driver's behaviors, we design a light and fast neural network (LFNN). On this basis, we construct a convolutional two-stream interactive network framework. One stream is used to acquire the spatial information of hand appearance; the other stream is used to obtain hand movement's temporal information. The features generated by the two streams are fused and classified through a short, interactive connection network. Our network structure has been tested on the CVRR-HANDS 3D data set. The accuracy reaches up to 96.5%, which obtains an obvious improvement compared with state of the art.