Video-based driver action recognition via hybrid spatial-temporal deep learning framework

Driver action recognition aims to distinguish normal driver action and some abnormal driver actions such as leaving the wheel, talking on the phone, diving with smoking, etc. For the purpose of traffic safety, studies on the computer vision technologies for driver action recognition have become especially meaningful. However, this issue is far from being solved, mainly due to the subtle variations between different driver action classes. In this paper, we present a new video-based driver action recognition approach based on the hybrid spatial-temporal deep learning framework. Specifically, we first design an encoder-decoder spatial-temporal convolutional neural network (EDSTCNN) to capture short-term spatial-temporal representation of driver actions jointly with optical flow prediction. Second, we exploit the feature refinement network (FRN) to refine the short-term driver action feature. Then, convolutional long short-term memory network (ConvLSTM) is employed for long-term spatial-temporal fusion. Finally, the fully connected neural network (FCNN) is used for final driver action recognition. In our experiment, we validate the performance of the proposed framework on our self-created datasets, including a simulated driving dataset and a real driving dataset. Extensive experimental results illustrate that the proposed hybrid spatial-temporal deep learning framework obtains the highest accuracy in multiple driver action recognition datasets (98.9% on SEU-DAR-V1 dataset and 97.0% on SEU-DAR-V2 dataset).