Skeleton Based Action Recognition
175 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
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.
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in general scenarios.
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition
Contrastive learning has achieved great success in skeleton-based action recognition.
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning
This method is known to be successful, but under very high pruning regimes, it suffers from topological inconsistency which renders the extracted subnetworks disconnected, and this hinders their generalization ability.
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition
Graph convolutional networks have been widely used in skeleton-based action recognition.
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation
Existing skeleton-based action recognition methods typically follow a centralized learning paradigm, which can pose privacy concerns when exposing human-related videos.
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks
Our research sheds light on the benefits of combining skeleton joints with object information for human action recognition in assembly tasks.
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition
Recently, significant achievements have been made in skeleton-based human action recognition with the emergence of graph convolutional networks (GCNs).
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition
Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs.
