Skarimva: Skeleton-based Action Recognition is a Multi-view Application

2026-02-26

AI Summary

This work addresses the limitations of existing skeleton-based action recognition methods, which suffer from inaccurate 3D pose estimation due to reliance on single-view inputs. For the first time, this study systematically demonstrates that multi-view skeleton reconstruction substantially enhances action recognition performance. By capturing synchronized data from multiple calibrated cameras and applying triangulation to generate high-fidelity 3D skeletons, the authors integrate these refined inputs into state-of-the-art action recognition models for comprehensive evaluation. Experimental results show a significant improvement in recognition accuracy, underscoring the critical role of input data quality. The paper further advocates for adopting multi-view setups as a standard experimental paradigm in the field, offering both notable performance gains and favorable cost-effectiveness for real-world deployment.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Video Understanding & Activity Analysis
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Human action recognition plays an important role when developing intelligent interactions between humans and machines. While there is a lot of active research on improving the machine learning algorithms for skeleton-based action recognition, not much attention has been given to the quality of the input skeleton data itself. This work demonstrates that by making use of multiple camera views to triangulate more accurate 3D~skeletons, the performance of state-of-the-art action recognition models can be improved significantly. This suggests that the quality of the input data is currently a limiting factor for the performance of these models. Based on these results, it is argued that the cost-benefit ratio of using multiple cameras is very favorable in most practical use-cases, therefore future research in skeleton-based action recognition should consider multi-view applications as the standard setup.

Problem

• Skeleton-based Action Recognition
• Input Data Quality
• Multi-view Application
• 3D Skeleton Accuracy

Innovation

• multi-view
• 3D skeleton
• action recognition
• triangulation
• input data quality