Pedestrian trajectory prediction is vital for transportation systems. Generally we can divide pedestrian behavior modeling into two categories, i.e., knowledge-driven and data-driven. The former might bring expert bias, and it sometimes generates unrealistic pedestrian movement due to unnecessary repulsive forces. The latter approach is popular nowadays but most existing neural networks, including fully connected long short-term memory (LSTM) networks, use a 1D vector to model their input and state. The shortcoming is that these works cannot learn spatial information about pedestrians, especially in a dense crowd. To tackle this, we propose to use tensors to represent essential environment features of pedestrians. Accordingly, a convolutional LSTM is designed and deepened to predict spatiotemporal trajectory sequences. As the tensor and convolution can learn better spatiotemporal interactions among pedestrians and environments, experimental results show that the proposed network can estimate more realistic trajectories for a dense crowd in evacuation and counterflow.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network


    Contributors:
    Song, Xiao (author) / Chen, Kai (author) / Li, Xu (author) / Sun, Jinghan (author) / Hou, Baocun (author) / Cui, Yong (author) / Zhang, Baochang (author) / Xiong, Gang (author) / Wang, Zilie (author)


    Publication date :

    2021-06-01


    Size :

    5385372 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Social graph convolutional LSTM for pedestrian trajectory prediction

    Yutao Zhou / Huayi Wu / Hongquan Cheng et al. | DOAJ | 2021

    Free access

    Social graph convolutional LSTM for pedestrian trajectory prediction

    Zhou, Yutao / Wu, Huayi / Cheng, Hongquan et al. | Wiley | 2021

    Free access

    A Posture Features Based Pedestrian Trajectory Prediction with LSTM

    Kao, I-Hsi / Zhou, Xiao / Chen, I-Ming et al. | IEEE | 2021


    Crossing-Road Pedestrian Trajectory Prediction via Encoder-Decoder LSTM

    Xue, Peixin / Liu, Jianyi / Chen, Shitao et al. | IEEE | 2019


    Street-crossing pedestrian trajectory prediction method based on SFM-LSTM neural network model

    ZHANG XI / YIN CHENGLIANG / ZHAO BAIXUAN et al. | European Patent Office | 2022

    Free access