Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition

Negar Heidari; Alexandros Iosifidis

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arXiv · 2011.05668

Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition

Negar Heidari·Alexandros Iosifidis

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Abstract

Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the GCN-based methods in this area train a deep feed-forward network with a fixed topology that leads to high computational complexity and restricts their application in low computation scenarios. In this paper, we propose a method to automatically find a compact and problem-specific topology for spatio-temporal graph convolutional networks in a progressive manner. Experimental results on two widely used datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance compared to the state-of-the-art methods with much lower computational complexity.

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BibTeX RIS

Negar Heidari, Alexandros Iosifidis. 2020-11-11. Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition. https://arxiv.org/abs/2011.05668

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