ST CrossingPose: a spatial-temporal graph convolutional network for skeleton-based pedestrian crossing intention prediction
Author(s)
Zhang, Xingchen
Angeloudis, Panagiotis
Demiris, Yiannis
Type
Journal Article
Abstract
Pedestrian crossing intention prediction is crucial for the safety of pedestrians in the context of both autonomous and conventional vehicles and has attracted widespread interest recently. Various methods have been proposed to perform pedestrian crossing intention prediction, among which the skeleton-based methods have been very popular in recent years. However, most existing studies utilize manually designed features to handle skeleton data, limiting the performance of these methods. To solve this issue, we propose to predict pedestrian crossing intention based on spatial-temporal graph convolutional networks using skeleton data (ST CrossingPose). The proposed method can learn both spatial and temporal patterns from skeleton data, thus having a good feature representation ability. Extensive experiments on a public dataset demonstrate that the proposed method achieves very competitive performance in predicting crossing intention while maintaining a fast inference speed. We also analyze the effect of several factors, e.g., size of pedestrians, time to event, and occlusion, on the proposed method.
Date Issued
2022-11-01
Date Acceptance
2022-04-22
Citation
IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (11), pp.20773-20782
ISSN
1524-9050
Publisher
Institute of Electrical and Electronics Engineers
Start Page
20773
End Page
20782
Journal / Book Title
IEEE Transactions on Intelligent Transportation Systems
Volume
23
Issue
11
Copyright Statement
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Publication Status
Published
Date Publish Online
2022-07-19