Detection of Road Accidents Using Synthetically Generated Multi-Perspective Accident Videos

Abstract

Road accidents are often caused by short abnormal events, including traffic violations, abrupt change in vehicular motion, driver fatigue, etc. Observing an accident event from the right camera perspective plays a crucial role while detecting accidents. However, it may not be possible to capture such abnormal events from a limited camera perspective. We present a deep learning framework to analyze the accident events recorded from multiple perspectives. First, we estimate feature similarity in videos recorded from multiple perspectives. We then divided the video samples into high and low feature similarity groups. Next, we extract spatio-temporal features from each group using two-branch DCNNs and fuse them using a rank-based weighted average pooling strategy followed by classification. We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform. Most of the existing road accident datasets use egocentric views or they are captured in fixed camera setups. However, our dataset is large and multi-perspective that can be used to validate ITS-related tasks such as accident detection, accident localization, traffic monitoring, etc. The dataset contains 400 accident events with a total of 2000 videos. We provide temporal annotations of all videos. The proposed framework and the dataset have been cross-validated with latest accident detection baselines trained on real-world road accident videos and vice-versa. The sub-optimal detection accuracy obtained using the baselines indicates that the proposed framework and the dataset can be useful for ITS related research. Code and dataset is available at: https://github.com/draxler1/MP-RAD-Dataset-ITS-