In this paper, we transfer some techniques used in computer vision to crowd counting and verify whether they are valid. These techniques are attention mechanism, using larger receptive filed and combination of two different ways ——MSE loss and Bayesian loss. Experiments show that channel attention and spatial attention is not helpful for crowd counting; using larger receptive filed can improve model performance and context-aware method is better than SPP block; combination of two different ways is also not helpful for crowd counting.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Are attention, larger receptive filed and multi task loss good for crowd counting?


    Beteiligte:
    Wang, Hua (Autor:in) / Chen, Shaodong (Autor:in) / Miao, Zhonghua (Autor:in)


    Erscheinungsdatum :

    20.10.2021


    Format / Umfang :

    1045479 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles

    Zhang, Yunqing / Zhang, Xiaotong / Liu, Kunhua et al. | IEEE | 2023


    WiFi-Crowd Spy: A novel crowd-counting system

    Collaguazo, Adriana / Estrada, Rebeca / Valeriano, Irving et al. | IEEE | 2022


    A Crowd Counting Framework Combining with Crowd Location

    Jin Zhang / Sheng Chen / Sen Tian et al. | DOAJ | 2021

    Freier Zugriff

    Multi-view branch-shared subway platform crowd counting method

    Liu, Guoyan / Zuo, Jing | British Library Conference Proceedings | 2023