Skeleton Based Action Recognition
174 papers with code • 34 benchmarks • 29 datasets
Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.
( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )
Benchmarks
These leaderboards are used to track progress in Skeleton Based Action Recognition
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Libraries
Use these libraries to find Skeleton Based Action Recognition models and implementationsDatasets
UCF101
Kinetics
HMDB51
Kinetics 400
NTU RGB+D
JHMDB
Penn Action
NTU RGB+D 120
SBU
UT-Kinect
Latest papers with no code
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition
To address this issue, we propose an innovative Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation (MK-SGN).
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition
Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN).
LLMs are Good Action Recognizers
Motivated by this, we propose a novel LLM-AR framework, in which we investigate treating the Large Language Model as an Action Recognizer.
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner
Most existing one-shot skeleton-based action recognition focuses on raw low-level information (e. g., joint location), and may suffer from local information loss and low generalization ability.
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition
Skeleton-based action recognition has attracted much attention, benefiting from its succinctness and robustness.
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition
We propose the first continual graph learning benchmark for spatio-temporal graphs and use it to benchmark well-known CGL methods in this novel setting.
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition
To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton based action recognition is first investigated.
READS-V: Real-time Automated Detection of Epileptic Seizures from Surveillance Videos via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection system can significantly benefit patients.
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System
In this study, the effectiveness of VIT for skeleton-based action recognition is examined and its robustness on the pseudo-image representation scheme is investigated.
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes
In manufacturing sectors such as textiles and electronics, manual processes are a fundamental part of production.
