Environment perception and object recognition are the key to realize cooperative vehicle infrastructure system. This paper proposes an object recognition method based on roadside LiDAR, which realizes pedestrian, bicycle and vehicle recognition through feature extraction and convolutional neural networks. First, the DBSCAN clustering algorithm is used to segment the point clouds from LiDAR. In order to decrease space complexity, the traditional features are optimized by feature selection. Then, a lightweight VGGNet network is built as the recognition network, and the self-attention mechanism is added to improve the representation ability of features and recognition accuracy. Experimental results show that the proposed method has a recognition rate of 87% for common traffic objects, which has good robustness and real-time performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Common Traffic Object Recognition Method Based on Roadside LiDAR


    Contributors:
    Xu, YuLin (author) / Liu, Wei (author) / Qi, Yong (author) / Hu, YiHan (author) / Zhang, Weibin (author)


    Publication date :

    2022-11-11


    Size :

    762261 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Advanced traffic conflict evaluation method with roadside LiDAR data

    Hou, Fujin / Cheng, Zhiheng / Li, Yan et al. | SPIE | 2024


    Roadside object recognition apparatus

    OKADA MASAYA / KUMANO SHUNYA / UEMATSU TAKUMI et al. | European Patent Office | 2021

    Free access

    ROADSIDE OBJECT RECOGNITION DEVICE

    OKADA MASAYA / KUMANO TOSHIYA / UEMATSU TAKUMI et al. | European Patent Office | 2019

    Free access

    ROADSIDE OBJECT RECOGNITION APPARATUS

    OKADA MASAYA / KUMANO SHUNYA / UEMATSU TAKUMI et al. | European Patent Office | 2019

    Free access

    Automatic Background Construction and Object Detection Based on Roadside LiDAR

    Zhang, Zhenyao / Zheng, Jianying / Xu, Hao et al. | IEEE | 2020