Deep learning based detection and localization of road accidents from traffic surveillance videos

Real-world traffic surveillance videos need continuous supervision to monitor and take appropriate actions in case of fatal accidents. However, continuously monitoring them with human supervision is error prone and tedious. Therefore, a deep learning approach for automatic detection and localization of road accidents has been proposed by formulating the problem as anomaly detection. The method follows one-class classification approach and applies spatio-temporal autoencoder and sequence-to-sequence long short-term memory autoencoder for modeling spatial and temporal representations in the video. The model is executed on a real-world video traffic surveillance datasets and significant results have been achieved both qualitatively and quantitatively. (C) 2021 Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences.

Automated summary

This paper proposes a deep learning approach for automatic detection and localization of road accidents by modeling spatial and temporal representations in the video using autoencoders. The method achieves significant results on real-world video traffic surveillance datasets.