Self-driving technology and safety monitoring devices in intelligent transportation systems require superb capacity for context awareness. Accurately inferring the counts of crowds and vehicles are the two practical and fundamental tasks in the transportation system. However, the scale variation and background interference in the traffic image hinder the counting performance. To solve the aforementioned problems, a scale region recognition network (SRRNet) is proposed in this paper. It has two key components, termed scale level awareness (SLA) module and object region recognition (ORR) module. The SLA module aims to encode the representations at multiple scales, which are beneficial to address the scale variation. The ORR module is designed to suppress background interference through the visual attention mechanism. Extensive experimental results on four crowd counting datasets and five vehicle counting datasets have demonstrated the superiority of the proposed SRRNet in both counting accuracy and robustness compared with the mainstream competitors. Meanwhile, substantial ablation studies have proved the effectiveness of the proposed SLA and ORS modules.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Scale Region Recognition Network for Object Counting in Intelligent Transportation System


    Contributors:
    Guo, Xiangyu (author) / Gao, Mingliang (author) / Zhai, Wenzhe (author) / Li, Qilei (author) / Jeon, Gwanggil (author)

    Published in:

    Publication date :

    2023-12-01


    Size :

    4662670 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning

    Meng, Yanda / Bridge, Joshua / Zhao, Yitian et al. | IEEE | 2023


    Intelligent transportation system based on image recognition

    LU GUANGYUN / CHENG JUNWEI / NI ZHIPING et al. | European Patent Office | 2024

    Free access

    NSSNet: Scale-Aware Object Counting With Non-Scale Suppression

    Liu, Liang / Cao, Zhiguo / Lu, Hao et al. | IEEE | 2022


    Passenger counting for a transportation system

    TUDI SANDEEP REDDY | European Patent Office | 2020

    Free access