Abstract City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Counting people in the crowd using social media images for crowd management in city events


    Beteiligte:
    Gong, V. X. (Autor:in) / Daamen, W. (Autor:in) / Bozzon, A. (Autor:in) / Hoogendoorn, S. P. (Autor:in)

    Erschienen in:

    Transportation ; 48 , 6 ; 3085-3119


    Erscheinungsdatum :

    2021




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.80$jVerkehrswesen$jTransportwesen: Allgemeines / 55.80 Verkehrswesen, Transportwesen: Allgemeines / 74.75$jVerkehrsplanung$jVerkehrspolitik / 74.75 Verkehrsplanung, Verkehrspolitik



    A Crowd Counting Framework Combining with Crowd Location

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

    Freier Zugriff

    WiFi-Crowd Spy: A novel crowd-counting system

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


    Crowd of oz : A crowd-powered social robotics system for stress management

    Abbas, Tahir / Khan, Vassilis Javed / Gadiraju, Ujwal et al. | TIBKAT | 2020

    Freier Zugriff

    Crowd of oz : A crowd-powered social robotics system for stress management

    Abbas, Tahir / Khan, Vassilis Javed / Gadiraju, Ujwal et al. | BASE | 2020

    Freier Zugriff

    Drone-SCNet: Scaled Cascade Network for Crowd Counting on Drone Images

    Elharrouss, Omar / Almaadeed, Noor / Abualsaud, Khalid et al. | IEEE | 2021