Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios
Document Type
Article
Publication Date
3-2022
Publication Title
IEEE Transactions on Intelligent Transportation Systems
Abstract
With the rapid progress of autonomous driving and advanced driver assistance systems, there are growing efforts to promote their safety in natural driving scenarios, especially for the detection of the traffic accidents. However, because of the dynamic camera motion and complex scene in driving situations, traffic accident detection is still challenging. In this work, we aim to give the ability of Traffic Accident Detection for driving systems by proposing a Self-Supervised Consistency learning framework, termed as SSC-TAD, that involves the appearance, motion, and context consistency learning. The key formulation is to find the inconsistency of video frames, object locations and the spatial relation structure of scene temporally between different frames captured by the dashcam videos. Within this field, different from the previous works which concentrate on predicting the future object locations or frames, we further focus on predicting the visual scene context in driving scenarios and detecting the traffic accident by considering the temporal frame consistency, temporal object location consistency, and the spatial-temporal relation consistency of road participants. In this work, this formulation is fulfilled by a collaborative multi-task consistency learning network and the visual scene context feature is represented by a graph convolution network. The superiority to the state-of-the-art is verified by exhaustive evaluations on two large scale datasets, i.e., the AnAn Accident Detection (A3D) dataset and DADA-2000 dataset collected recently.
Repository Citation
Fang, Jianwu; Qiao, Jiahuan; Bai, Jie; Yu, Hongkai; and Xue, Jianru, "Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios" (2022). Electrical and Computer Engineering Faculty Publications. 496.
https://engagedscholarship.csuohio.edu/enece_facpub/496
DOI
10.1109/TITS.2022.3157254
Comments
This work was supported in part by the National Natural Science Foundation of China under Grant 62036008; in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2022JM-309; and in part by the Fundamental Research Funds for the Central Universities, CHD, under Grant 300102320202.