Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset Using Manipulating Conditional Style Translation

In the last decade, advanced self-driving system brings significantly improvement technology on various aspects such as efficiency, convenience, and transportation safety system to contribute the global society impacts around the world. To develop it, many researchers are focusing to alert all possible traffic risk cases from closed-circuit television (CCTV) and dashboardmounted cameras. Most of these methods focused on identifying frame-by-frame in which an anomaly is 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 are no different between accident and near-miss on the time before accident happened, so that we re-define the definition of accident on DADA-2000 dataset together with nearmiss and also extend start and end time of accident duration to precisely cover all ego-motions during incident. Unlike previous works, proposed system is to classify all possible traffic risk accidents including near-miss to give more critical information for real-world driving assistance systems. Due to limited annotating video availability, we augment re-annotation DADA-2000 dataset using manipulating video style translation to increase number of traffic risk accident videos and to generalize performance of video classification model on different types of conditions. In evaluation, the proposed method achieved significantly improvement result by 10.25 % positive margin from baseline model for accuracy on cross validation analysis. Quantitative evaluation based on our re-annotation shows that the proposed method is valuable for computer vision community to train their models to produce better traffic risk classification.