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Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation

Peng Chen1, Weiwei Zhang1,*, Ziyao Xiao1, Yongxiang Tian2

1 School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
2 Shanghai Fire Research Institute of MEM, Shanghai, 200032, China

* Corresponding Author: Weiwei Zhang. Email: email

Computer Modeling in Engineering & Sciences 2022, 130(1), 97-109.


Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving. This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively. Using 2D region proposals in an RGB image, this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network (FPS-Net) and feature extraction network (FE-Net). Subsequently, the encoder-decoder network (ED-Net) implements 3D-oriented bounding box (OBB) regression. Meanwhile, the adaptive least square regression (ALSR) method is proposed to split 3D OBB. Finally, the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem (SST). In the experiments of KITTI benchmark, our proposed 3D object detector outperforms other state-of-the-art methods. Meanwhile, collision detection algorithm achieves the satisfactory performance of 91.8% accuracy on our SHTA dataset.


Cite This Article

Chen, P., Zhang, W., Xiao, Z., Tian, Y. (2022). Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation. CMES-Computer Modeling in Engineering & Sciences, 130(1), 97–109.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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