Rethinking the ST-GCNs for 3D skeleton-based human action recognition

[摘要] The skeletal data has been an alternative for the human action recognition task as it provides more com-pact and distinct information compared to the traditional RGB input. However, unlike the RGB input, the skeleton data lies in a non-Euclidean space that traditional deep learning methods are not able to use their fullest potential. Fortunately, with the emerging trend of Geometric deep learning, the spatial -temporal graph convolutional network (ST-GCN) has been proposed to deal with the action recognition problem from skeleton data. ST-GCN and its variants fit well with skeleton-based action recognition and are becoming the mainstream frameworks for this task. However, the efficiency and the performance of the task are hindered by either fixing the skeleton joint correlations or providing a computational expen-sive strategy to construct a dynamic topology for the skeleton. We argue that many of these operations are either unnecessary or even harmful for the task. By theoretically and experimentally analysing the state-of-the-art ST-GCNs, we provide a simple but efficient strategy to capture the global graph correla-tions and thus efficiently model the representation of the input graph sequences. Moreover, the global graph strategy also reduces the graph sequence into the Euclidean space, thus a multi-scale temporal fil-ter is introduced to efficiently capture the dynamic information. With the method, we are not only able to better extract the graph correlations with much fewer parameters (only 12.6% of the current best), but we also achieve a superior performance. Extensive experiments on current largest 3D datasets, NTU-RGB+D and NTU-RGB+D 120, demonstrate the ability of our network to perform efficient and lightweight priority on this task. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

[发布日期] 2021-09-24

[关键词] Human action recognition;ST-GCNs;Dynamic graph modeling;Deep neural networks