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Hybrid Traffic Accident Classification Models

Author

Listed:
  • Yihang Zhang

    (Department of Autonomous Things Intelligence, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

  • Yunsick Sung

    (Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea)

Abstract

Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. This paper proposes a CCTV frame-based hybrid traffic accident classification model that enables the identification of whether a frame includes accidents by generating object trajectories. The proposed model utilizes a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) to extract latent representations from each frame and corresponding trajectories. The fusion of frame and trajectory features was performed to improve the traffic accident classification ability of the proposed hybrid method. In the experiments, the Car Accident Detection and Prediction (CADP) dataset was used to train the hybrid model, and the accuracy of the model was approximately 97%. The experimental results indicate that the proposed hybrid method demonstrates an improved classification performance compared to traditional models.

Suggested Citation

  • Yihang Zhang & Yunsick Sung, 2023. "Hybrid Traffic Accident Classification Models," Mathematics, MDPI, vol. 11(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1050-:d:1073367
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    References listed on IDEAS

    as
    1. Minhee Kang & Wooseop Lee & Keeyeon Hwang & Young Yoon, 2022. "Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment," Sustainability, MDPI, vol. 14(15), pages 1-19, August.
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