The fundamental diagramfundamental diagram of pedestrian flow, which describes a relation between pedestrian velocity and crowd density, is an important means forpedestrian dynamicsPedestrian dynamics analysis. Some recent work calculates the fundamental diagram of pedestrian flow by tracking each pedestrian in the crowd from video recordings. However, such methods are limited in representation of crowd density and hard to achieve a real-timeReal-time analysis. To address this problem, this work proposes a novel convolutional neural network-based framework, called deep fundamental diagram network, for real-time pedestrian dynamics analysis. Our proposed framework is consisted of two parts, the multi-scale recursive convolutional neural network (MSR-Net) and an optical flowOptical flow module, accounting for density distribution estimation and pedestrian motion prediction respectively. Specifically, MSR-Net is presented to learn the direct mapping from the input image of pedestrian flow to the output map of crowd density. Optical flow method is introduced to predict the velocity and direction of pedestrian in real-timeReal-time. In this way, by aligning the position of pedestrian density map we are able to obtain the fundamental diagramfundamental diagram, which shows good agreement with the ones from classical methods but higher computational efficiency. Simultaneously, deep fundamental diagram network can detect anomaly activity of pedestrian (In this work, the anomaly is defined as sudden stop and acceleration, reverse walk.), which is also meaningful for crowd analysis.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Fundamental Diagram Network for Real-Time Pedestrian Dynamics Analysis


    Additional title:

    Springer Proceedings Phys.


    Contributors:
    Zuriguel, Iker (editor) / Garcimartin, Angel (editor) / Cruz, Raul (editor) / Ma, Qing (author) / Kang, Yu (author) / Song, Weiguo (author) / Cao, Yang (author) / Zhang, Jun (author)

    Published in:

    Traffic and Granular Flow 2019 ; Chapter : 24 ; 195-203


    Publication date :

    2020-11-17


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Deep Fundamental Diagram Network for Real-Time Pedestrian Dynamics Analysis

    Ma, Qing / Kang, Yu / Song, Weiguo et al. | British Library Conference Proceedings | 2020


    Deep Fundamental Diagram Network for Real-Time Pedestrian Dynamics Analysis

    Ma, Qing / Kang, Yu / Song, Weiguo et al. | TIBKAT | 2020


    Bidirectional pedestrian fundamental diagram

    Flötteröd, Gunnar / Lämmel, Gregor | Elsevier | 2014


    Bidirectional pedestrian fundamental diagram

    Flötteröd, Gunnar | Online Contents | 2015


    Pedestrian Fundamental Diagram in Between Normal Walk and Crawling

    Ma, Jian / Shi, Dongdong / Li, Tao | British Library Conference Proceedings | 2020