Application of vehicle detection with YOLOv3 in road surveillance video

Lianxiang Zhu; Lijuan Xu

doi:10.1117/12.2626939

2 March 2022 Application of vehicle detection with YOLOv3 in road surveillance video

Lianxiang Zhu, Lijuan Xu

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 121580S (2022) https://doi.org/10.1117/12.2626939
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Vehicle detection from traffic monitoring video, which lays the foundation to serials consequential operations such as vehicle counting and accident detection, is an essential part of traffic monitoring system. Traditional target detection methods always have some kind of drawbacks more or less, while target detection based on deep learning has the benefits of more abundant target feature extraction, thus much higher detection accuracy can be achieved, of which YOLO v3 is a typical representative. In this paper, the model structure and principles of YOLOv3 is analyzed in depth firstly, and then its application to vehicle detection in road surveillance video is carried out with the discussion of some existing problems.

Citation Download Citation

Lianxiang Zhu and Lijuan Xu "Application of vehicle detection with YOLOv3 in road surveillance video", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 121580S (2 March 2022); https://doi.org/10.1117/12.2626939

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