IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Harnessing Time: A Skeleton Temporal Fusion Graph Convolutional Network for Elderly Action Recognition
Qingqi ZHANGXiaoan BAORen WUMitsuru NAKATAQi-Wei GE
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JOURNAL FREE ACCESS Advance online publication

Article ID: 2024MAP0005

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Abstract

Elderly action recognition is a more challenging task due to the fact that elderly individuals move with small amplitude and long duration of actions. There are two pivotal issues that warrant further exploration including the development of enhanced temporal feature representations and the expansion of convolutional models' capacity to capture long-range temporal features. In this paper, we propose a novel Skeleton Temporal Fusion Graph Convolutional Network (STF-GCN) for skeleton-based elderly action recognition, which effectively models advanced temporal feature representations. More specifically, the STF-GCN employs three encoding strategies to integrate two types of temporal feature representations. These strategies are designed to capture the intricacies of motion dynamics and the subtleties in action variations, enabling a more accurate and robust recognition of elderly actions. Furthermore, we propose a Skeleton Temporal Fusion (STF) module to highlight the temporal feature representations, employing a structure that alternates between large and small kernel convolutions to achieve various effective receptive fields. The integration of large kernel convolutions allows our model to perceive an expanded temporal context, enhancing its ability to deeply understand action dynamics. Our evaluation demonstrates that the STF-GCN achieves state-of-the-art performance on the largest elderly dataset, ETRI-Activity3D. Additionally, more extensive experimental results show that the STF-GCN is also comparable to other methods in general action recognition tasks.

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© 2024 The Institute of Electronics, Information and Communication Engineers
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