An Efficient Framework for Human Action Recognition Based on Graph Convolutional Networks

Abstract

This paper presents a novel framework for skeleton-based Human Action Recognition (HAR) based on Graph Convolution Networks (GCNs). The proposed framework aims to increase human action recognition performance of GCN-based methods by incorporating a missing-joint-handling pre-processing step and a novel adjacency matrix construction method in a single human action recognition pipeline. The missing-joint-handling pre-processing step is utilized to infer missing data in the input sequence, which may occur due to imperfect skeleton extraction, based on imputation methods. The novel adjacency matrix construction method is executed offline to compute an improved weighted adjacency matrix specifically designed for HAR, which is utilized in every layer of the employed GCN. Moreover, both the pre-processing step and the adjacency construction method can be utilized along with any GCN architecture, allowing any GCN-based HAR method to be employed in the proposed framework. Experimental evaluation on two public datasets indicate favorable human action classification scores compared to the employed baseline and all competing methods both for 2D and 3D skeleton-based human action recognition, while using a GCN architecture with less learnable parameters.