Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition
This work addresses traffic security by enhancing surveillance of driving behavior, but it is incremental as it builds on existing two-stream CNN methods with new fusion strategies.
The paper tackled the problem of recognizing driving behaviors from video to improve traffic safety by proposing a two-stream CNN framework that fuses spatial and temporal information, achieving noticeable performance improvements on a simulated dataset of 1237 videos with 6 behaviors.
Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.