  To develop the advanced self-driving systems, many researchers are focusing
to alert all possible traffic risk cases from closed-circuit television (CCTV)
and dashboard-mounted cameras. Most of these methods focused on identifying
frame-by-frame in which an anomaly has occurred, but they are unrealized, which
road traffic participant can cause ego-vehicle leading into collision because
of available annotation dataset only to detect anomaly on traffic video.
Near-miss is one type of accident and can be defined as a narrowly avoided
accident. However, there is no difference between accident and near-miss at the
time before the accident happened, so our contribution is to redefine the
accident definition and re-annotate the accident inconsistency on DADA-2000
dataset together with near-miss. By extending the start and end time of
accident duration, our annotation can precisely cover all ego-motions during an
incident and consistently classify all possible traffic risk accidents
including near-miss to give more critical information for real-world driving
assistance systems. The proposed method integrates two different components:
conditional style translation (CST) and separable 3-dimensional convolutional
neural network (S3D). CST architecture is derived by unsupervised
image-to-image translation networks (UNIT) used for augmenting the
re-annotation DADA-2000 dataset to increase the number of traffic risk accident
videos and to generalize the performance of video classification model on
different types of conditions while S3D is useful for video classification to
prove dataset re-annotation consistency. In evaluation, the proposed method
achieved a significant improvement result by 10.25% positive margin from the
baseline model for accuracy on cross-validation analysis.
