# Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks

arXiv: https://arxiv.org/abs/2108.01309 · HTML: https://mlsanity.com/paper/2108.01309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1902.09130/) (2019)

## Built on by (most cited first, 1 of 1)

- [Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion](https://mlsanity.com/paper/2111.03106/) (2021)

## Abstract (arXiv)

A skeleton representation of the human body has been proven to be effective for this task. The skeletons are presented in graphs form-like. However, the topology of a graph is not structured like Euclidean-based data. Therefore, a new set of methods to perform the convolution operation upon the skeleton graph is presented. Our proposal is based upon the ST-GCN framework proposed by Yan et al. [1]. In this study, we present an improved set of label mapping methods for the ST-GCN framework. We introduce three split processes (full distance split, connection split, and index split) as an alternative approach for the convolution operation. To evaluate the performance, the experiments presented in this study have been trained using two benchmark datasets: NTU-RGB+D and Kinetics. Our results indicate that all of our split processes outperform the previous partition strategies and are more stable during training without using the edge importance weighting additional training parameter. Therefore, our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.