MSTCNN: multi-modal spatio-temporal convolutional neural network for pedestrian trajectory prediction

Haifeng Sang; Wangxing Chen; Haifeng Wang; Jinyu Wang

doi:10.1007/s11042-023-15989-4

ScienceGate Book Chapters

JOURNAL ARTICLE

MSTCNN: multi-modal spatio-temporal convolutional neural network for pedestrian trajectory prediction

Haifeng Sang Wangxing Chen Haifeng Wang Jinyu Wang

Year: 2023 Journal: Multimedia Tools and Applications Vol: 83 (3)Pages: 8533-8550 Publisher: Springer Science+Business Media

DOI: 10.1007/s11042-023-15989-4

Get Full-Text PDF Get Analytical Report

Keywords:

Pedestrian Computer science Trajectory Modal Convolutional neural network Gaussian Artificial intelligence Path (computing) Data mining Machine learning

Metrics

Cited By

2.13

FWCI (Field Weighted Citation Impact)

Refs

0.82

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Autonomous Vehicle Technology and Safety

Physical Sciences → Engineering → Automotive Engineering

Traffic and Road Safety

Physical Sciences → Engineering → Safety, Risk, Reliability and Quality

Video Surveillance and Tracking Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

MSTCNN: multi-modal spatio-temporal convolutional neural network for pedestrian trajectory prediction

Metrics

Citation History

Topics

Related Documents

Social Spatio-Temporal Graph Convolutional Neural Network for Pedestrian Trajectory Prediction

Channel spatio-temporal convolutional network for pedestrian trajectory prediction

PTP-STGCN: Pedestrian Trajectory Prediction Based on a Spatio-temporal Graph Convolutional Neural Network

Spatio-Temporal Graph Convolutional Networks for Pedestrian Trajectory Prediction

REGION: Relevant Entropy Graph spatIO-temporal convolutional Network for Pedestrian Trajectory Prediction