  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.
