Driver anomalous driving behavior detection based on supervised contrastive learning

Zhonglun Li; Duan Jin

doi:10.1117/12.3021283

19 February 2024 Driver anomalous driving behavior detection based on supervised contrastive learning

Zhonglun Li, Duan Jin

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632Q (2024) https://doi.org/10.1117/12.3021283
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

In the context of vehicle operation, detecting and identifying abnormal driver behaviors is challenging due to the complex in-car environment, changing lighting, and varied driver postures. Our approach, utilizing supervised contrastive learning, categorizes driver behaviors as normal or abnormal. We employ depth images from the driver's front and above to address environmental complexities and improve accuracy. Our enhanced 3D-MobileNetV2architecture achieves impressive results on the DAD dataset test set, with a 94.18% accuracy rate and a 0.962AUC, validating the effectiveness of our method in driver anomaly detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhonglun Li and Duan Jin "Driver anomalous driving behavior detection based on supervised contrastive learning", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632Q (19 February 2024); https://doi.org/10.1117/12.3021283

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