3DCNN-Based Real-Time Driver Fatigue Behavior Detection in Urban Rail Transit

With the rapid development of urban rail transit, traffic safety has become the focus of attention and people are paying increasing attention to the prevention of fatigue driving. “Gesture and oral instructions of urban rail traffic drivers” is operational actions of drivers written in the Chinese metro operation specification. It is a method to prevent drivers from fatigue driving and ensure safety. However, there is a lack of scientific detection methods. We combine the standard traffic operational actions with fatigue action to construct a fatigue detection system that is suitable for the urban rail transit industry. The system includes a dynamic tracking model for the large-scale operation of rail transit drivers and a dual-input action discrimination model based on a three-dimensional convolutional neural network (3DCNN). The model sets the skipping frame and continuous frame as two inputs of the model, and extracts five channels of information from the two inputs. Dual-input multi-channel information enables the model to learn not only the spatial and temporal information of the entire action, but also the subtle changes of the action. First, we trained and validated the dual-input model based on a 3DCNN using the open dataset KTH, which contains several variations. Then, the model trained on KTH was migrated to our data using the transfer learning method, which saved training time and achieves an accuracy of 98.41%. This transfer learning scheme can also be applied when new categories are encountered in practice. Finally, we discussed and envisaged the future optimization of the system.