Feature pyramid graph convolutional networks for temporal action detection

Shanshan Du; Xiaomeng Zhang; Shanbang Zhu; Ruiwei Zhao; Weidong Xu; Rui Feng

doi:10.1117/12.2662623

28 December 2022 Feature pyramid graph convolutional networks for temporal action detection

Shanshan Du, Xiaomeng Zhang, Shanbang Zhu, Ruiwei Zhao, Weidong Xu, Rui Feng

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Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125060R (2022) https://doi.org/10.1117/12.2662623
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China

Abstract

In some long and untrimmed videos, locating the important and key segments can be a very challenging task for temporal action detection. Current methods make remarkable progress when it comes to RGB images. The aim of this paper is to develop a method with the assistance of dynamic model of human body skeletons to address this problem. To this end, we propose a Feature Pyramid Graph Convolutional Network (FP-GCN). The introduced model contains a Feature Encoding Module to encode skeleton data with graph convolutional networks, a Feature Pyramid Module to exploit the inherent pyramidal hierarchy and an Action Detection Module to generate the final prediction results of the detection. We experiment our approach on NTU RGB+D and THUMOS14 datasets and obtain a satisfactory result.

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Shanshan Du, Xiaomeng Zhang, Shanbang Zhu, Ruiwei Zhao, Weidong Xu, and Rui Feng "Feature pyramid graph convolutional networks for temporal action detection", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125060R (28 December 2022); https://doi.org/10.1117/12.2662623

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