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2020 | OriginalPaper | Chapter

DRGCN: Deep Relation GCN for Group Activity Recognition

Authors : Yiqiang Feng, Shimin Shan, Yu Liu, Zhehuan Zhao, Kaiping Xu

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Person to person relation is an essential clue for group activity recognition (GAR). And the relation graph and the graph convolution neural network (GCN) have become powerful presentation and processing tools of relationship. The previous methods are difficult to capture the complex relationship between people. We propose an end-to-end framework called Deep Relation GCN (DRGCN) for recognizing group activities by exploring the high-level relations between individuals. In DRGCN, we use a horizontal slicing strategy to layer each individual into smaller individual parts, then apply a deep GCN to learn the relation graph of these individual parts. We perform experiments on two widely used datasets and obtain competitive results that demonstrated the effectiveness of our method.

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Title: DRGCN: Deep Relation GCN for Group Activity Recognition
Authors: Yiqiang Feng
Shimin Shan
Yu Liu
Zhehuan Zhao
Kaiping Xu
Publisher: Springer International Publishing
Book: Neural Information Processing
Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63820-7_41

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