Cargando…

MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is an essential but challenging task. Social interactions between pedestrians have an immense impact on trajectories. A better way to model social interactions generally achieves a more accurate trajectory prediction. To comprehensively model the interactions between...

Descripción completa

Detalles Bibliográficos
Autores principales:	Liu, Shaohua, Liu, Haibo, Wang, Yisu, Sun, Jingkai, Mao, Tianlu
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Hindawi 2022
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019418/ https://www.ncbi.nlm.nih.gov/pubmed/35463224 http://dx.doi.org/10.1155/2022/4192367

Ejemplares similares

Proposal for the ALEPH DST and MDST
por: Boucrot, J, et al.
Publicado: (1988)

Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data
por: Liu, Shaohua, et al.
Publicado: (2021)

EGAT: Extended Graph Attention Network for Pedestrian Trajectory Prediction
por: Kong, Wei, et al.
Publicado: (2021)

New Systolic Array Algorithms and VLSI Architectures for 1-D MDST †
por: Chiper, Doru Florin, et al.
Publicado: (2023)

DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data
por: Zhang, Shao-Wu, et al.
Publicado: (2022)

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions
por: Alghodhaifi, Hesham, et al.
Publicado: (2023)

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization
por: Zhang, Xitong, et al.
Publicado: (2022)

Enumerating consistent sub-graphs of directed acyclic graphs: an insight into biomedical ontologies
por: Peng, Yisu, et al.
Publicado: (2018)

An Attention-Guided Spatiotemporal Graph Convolutional Network for Sleep Stage Classification
por: Li, Menglei, et al.
Publicado: (2022)

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
por: Oluwasanmi, Ariyo, et al.
Publicado: (2023)

Spatiotemporal adaptive attention graph convolution network for city-level air quality prediction
por: Liu, Hexiang, et al.
Publicado: (2023)

Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder
por: Zhang, Xiaodi, et al.
Publicado: (2021)

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data
por: Liu, Xinyi, et al.
Publicado: (2022)

Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model
por: Kim, Jungi, et al.
Publicado: (2022)

Attention-based message passing and dynamic graph convolution for spatiotemporal data imputation
por: Wang, Yifan, et al.
Publicado: (2023)

Pedestrian Detection with Multi-View Convolution Fusion Algorithm
por: Liu, Yuhong, et al.
Publicado: (2022)

Pedestrian Trajectory Prediction in Extremely Crowded Scenarios
por: Shi, Xiaodan, et al.
Publicado: (2019)

Author Correction: Attention-based message passing and dynamic graph convolution for spatiotemporal data imputation
por: Wang, Yifan, et al.
Publicado: (2023)

Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone
por: Qian, Jiuchao, et al.
Publicado: (2015)

Multilevel Typed Graph Transformations
por: Wolter, Uwe, et al.
Publicado: (2020)

Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction
por: Lin, Chien-Yu, et al.
Publicado: (2022)

Functional Objects in Urban Walking Environments and Pedestrian Trajectory Modelling
por: Lui, Andrew Kwok Fai, et al.
Publicado: (2023)

Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks
por: Reveles-Gómez, Luis C., et al.
Publicado: (2023)

Towards routine, city-scale accessibility metrics: Graph theoretic interpretations of pedestrian access using personalized pedestrian network analysis
por: Bolten, Nicholas, et al.
Publicado: (2021)

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
por: Marco Salas, Sergio, et al.
Publicado: (2022)

Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network
por: Zhang, Haoran, et al.
Publicado: (2023)

A microscopic simulation model for pedestrian-pedestrian and pedestrian-vehicle interactions at crosswalks
por: Liu, Manxia, et al.
Publicado: (2017)

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction
por: Sighencea, Bogdan Ilie, et al.
Publicado: (2021)

Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories
por: Moreno, Esteban, et al.
Publicado: (2023)

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network
por: Cheng, Quan, et al.
Publicado: (2023)

A multi-branch separable convolution neural network for pedestrian attribute recognition
por: Junejo, Imran N., et al.
Publicado: (2020)

SGCN: A Graph Sparsifier Based on Graph Convolutional Networks
por: Li, Jiayu, et al.
Publicado: (2020)

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction
por: Pu, Shilin, et al.
Publicado: (2022)

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones
por: Gao, Lulu, et al.
Publicado: (2022)

Pedestrian Detection with Semantic Regions of Interest
por: He, Miao, et al.
Publicado: (2017)

Generating Pedestrian Trajectories Consistent with the Fundamental Diagram Based on Physiological and Psychological Factors
por: Narang, Sahil, et al.
Publicado: (2015)

Image-Based Localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones
por: Zhou, Yan, et al.
Publicado: (2018)

GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
por: Peltola, Pekka, et al.
Publicado: (2018)

Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics
por: González, Leticia, et al.
Publicado: (2022)

Identification of social groups and waiting pedestrians at railway platforms using trajectory data
por: Küpper, Mira, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_dcqoc1n3jnrgj2lvdrcievbhbk