Abstract: In the process of driving，violations such as looking at mobile phones and talking with passengers
cause great hidden dangers to safe driving． In order to solve such problems，we propose a multi-angle behavior
recognition method，which can synchronously capture video of driving behaviors from three angles，construct a
multi-angle driver behavior data set，and use deep convolutional neural network to carry out recognition and
classification． The experimental results show the advantages of 3D CNN over 2D CNN in accuracy． And in
comparing input clip frames experiment，we found the stacked video frames will affect accuracy and in R2plus1D
model ( 3D convolution filter is decomposed into a separate component of time and space) ，realized driver
behavior based recognition accuracy of 87% from multiple perspectives．

Key words: computer vision, deep learning, behavior recognition, drivers, multiple perspectives