Traffic accident anticipation in driving videos aims to provide early warning of accidents and encourage accurate decision-making. Previous research has primarily focused on the spatial temporal correlation at the object level, but it lacks some explainable clues and is susceptible to severe environmental changes. Hence we propose a method that utilizes driver attention as an auxiliary factor for traffic accident anticipation (DA-TAA) to enhance model training in this work. Specifically, driver attention provides valuable insights into key areas closely related to safe driving. DA-TAA consists of a self-attention feature extraction module, a temporal GRU module, and a driver attention-guided accident prediction module. We employ attention mechanisms to explore driver attention cues for accident prediction. We train the model using the DADA-2000 dataset, which includes annotated driver attention per frame and evaluate its performance on both the DADA-2000 and CCD datasets. Our extensive experiments demonstrate that DA-TAA outperforms state-of-the-art methods in traffic accident anticipation.
Traffic Accident Anticipation via Driver Attention Auxiliary
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2023 ; Nanjing, China September 09, 2023 - September 11, 2023
Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) ; Chapter : 33 ; 348-360
2024-04-23
13 pages
Article/Chapter (Book)
Electronic Resource
English
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