# Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information

arXiv: https://arxiv.org/abs/2109.02376 · HTML: https://mlsanity.com/paper/2109.02376/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 56 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [Intel RealSense Stereoscopic Depth Cameras](https://mlsanity.com/paper/1705.05548/) (2017)
- [Spatial Temporal Transformer Network for Skeleton-based Action Recognition](https://mlsanity.com/paper/2012.06399/) (2020)
- [Learning a low-rank shared dictionary for object classification](https://mlsanity.com/paper/1602.00310/) (2016)

## Built on by (most cited first, 6 of 6)

- [Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition](https://mlsanity.com/paper/2307.07469/) (2023)
- [Understanding Spatio-Temporal Relations in Human-Object Interaction using Pyramid Graph Convolutional Network](https://mlsanity.com/paper/2410.07912/) (2024)
- [A Two-stream Hybrid CNN-Transformer Network for Skeleton-based Human Interaction Recognition](https://mlsanity.com/paper/2401.00409/) (2023)
- [Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network](https://mlsanity.com/paper/2207.05493/) (2022)
- [Towards Open-World Human Action Segmentation Using Graph Convolutional Networks](https://mlsanity.com/paper/2507.00756/) (2025)
- [Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation](https://mlsanity.com/paper/2507.00752/) (2025)

## Abstract (arXiv)

This paper propose a novel dictionary learning approach to detect event action using skeletal information extracted from RGBD video. The event action is represented as several latent atoms and composed of latent spatial and temporal attributes. We perform the method at the example of fall event detection. The skeleton frames are clustered by an initial K-means method. Each skeleton frame is assigned with a varying weight parameter and fed into our Gradual Online Dictionary Learning (GODL) algorithm. During the training process, outlier frames will be gradually filtered by reducing the weight that is inversely proportional to a cost. In order to strictly distinguish the event action from similar actions and robustly acquire its action unit, we build a latent unit temporal structure for each sub-action. We evaluate the proposed method on parts of the NTURGB+D dataset, which includes 209 fall videos, 405 ground-lift videos, 420 sit-down videos, and 280 videos of 46 otheractions. We present the experimental validation of the achieved accuracy, recall and precision. Our approach achieves the bestperformance on precision and accuracy of human fall event detection, compared with other existing dictionary learning methods. With increasing noise ratio, our method remains the highest accuracy and the lowest variance.