Vision-Based Traffic Accident Detection and Anticipation: A Survey

Fang, Jianwu; Qiao, Jiahuan; Xue, Jianru; Li, Zhengguo

doi:10.1109/tcsvt.2023.3307655

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2024

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Cited by 8 publications

References 136 publications

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MSAN: Multi-Scale Adaptive Network Guided by Human Attention for Accident Prediction

Qiao,

Yang,

2024

Lecture Notes in Computer Science

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MSAN: Multi-Scale Adaptive Network Guided by Human Attention for Accident Prediction

Qiao,

Yang,

2024

Lecture Notes in Computer Science

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TADS: a novel dataset for road traffic accident detection from a surveillance perspective

Chai,

Fang,

Liang

et al. 2024

J Supercomput

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Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

Adewopo,

Elsayed

2024

IEEE Access

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The dynamic and unpredictable nature of road traffic necessitates effective accident detection methods for enhancing safety and streamlining traffic management in smart cities. This paper offers a comprehensive exploration study of prevailing accident detection techniques, shedding light on the nuances of other state-of-the-art methodologies while providing a detailed overview of distinct traffic accident types like rear-end collisions, T-bone collisions, and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2D model architecture, a lightweight solution tailored explicitly for accident detection in smart city traffic surveillance systems by integrating RGB frames with optical flow information. Our experimental study's empirical analysis underscores our approach's efficacy, with the I3D-CONVLSTM2D RGB + Optical-Flow (Trainable) model outperforming its counterparts, achieving an impressive 87% Mean Average Precision (MAP). Our findings further elaborate on the challenges posed by data imbalances, particularly when working with a limited number of datasets, road structures, and traffic scenarios. Ultimately, our research illuminates the path towards a sophisticated vision-based accident detection system primed for real-time integration into edge IoT devices within smart urban infrastructures.

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Vision-Based Traffic Accident Detection and Anticipation: A Survey

Cited by 8 publications

References 136 publications

MSAN: Multi-Scale Adaptive Network Guided by Human Attention for Accident Prediction

MSAN: Multi-Scale Adaptive Network Guided by Human Attention for Accident Prediction

TADS: a novel dataset for road traffic accident detection from a surveillance perspective

Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

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