Physical Depth-aware Early Accident Anticipation: A Multi-dimensional Visual Feature Fusion Framework

• URL: http://arxiv.org/abs/2502.18496v1
• Date: Wed, 19 Feb 2025 09:07:38 GMT
• Title: Physical Depth-aware Early Accident Anticipation: A Multi-dimensional Visual Feature Fusion Framework
• Authors: Hongpu Huang, Wei Zhou, Chen Wang,
• Abstract summary: We propose a physical depth-aware learning framework that incorporates the monocular depth features generated by a large model named Depth-Anything.<n>The proposed framework captures early indicators of accidents through the analysis of interaction relationships between objects in sequential frames.<n> Experimental results on public effectiveness show that the proposed framework attains state-of-the-art performance.
• Score: 6.32591751662313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early accident anticipation from dashcam videos is a highly desirable yet challenging task for improving the safety of intelligent vehicles. Existing advanced accident anticipation approaches commonly model the interaction among traffic agents (e.g., vehicles, pedestrians, etc.) in the coarse 2D image space, which may not adequately capture their true positions and interactions. To address this limitation, we propose a physical depth-aware learning framework that incorporates the monocular depth features generated by a large model named Depth-Anything to introduce more fine-grained spatial 3D information. Furthermore, the proposed framework also integrates visual interaction features and visual dynamic features from traffic scenes to provide a more comprehensive perception towards the scenes. Based on these multi-dimensional visual features, the framework captures early indicators of accidents through the analysis of interaction relationships between objects in sequential frames. Additionally, the proposed framework introduces a reconstruction adjacency matrix for key traffic participants that are occluded, mitigating the impact of occluded objects on graph learning and maintaining the spatio-temporal continuity. Experimental results on public datasets show that the proposed framework attains state-of-the-art performance, highlighting the effectiveness of incorporating visual depth features and the superiority of the proposed framework.

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