Skeleton Based Action Recognition
206 papers with code • 34 benchmarks • 30 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
NTU RGB+D 120
Penn Action
UT-Kinect
PKU-MMD
Latest papers
Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition
In self-supervised skeleton-based action recognition, the mask reconstruction paradigm is gaining interest in enhancing model refinement and robustness through effective masking.
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Levigty/CMCS
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IEEE Transactions on Human-Machine Systems 2024
Inspired by SkeletonBYOL, this paper further presents a Cross-Model and Cross-Stream (CMCS) framework.
SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action Recognition
The 3D hand pose, together with information from object detection, is processed by a transformer-based action recognition network, resulting in an accuracy of 91. 73%, outperforming all state-of-the-art methods.
Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution
Spatial GCN performs information aggregation based on the topological structure of the human body, and structural GCN performs differentiation based on the similarity of edge node sequences.
Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
Skeleton-based action recognition has garnered significant attention due to the utilization of concise and resilient skeletons.
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders
Existing zero-shot skeleton-based action recognition methods utilize projection networks to learn a shared latent space of skeleton features and semantic embeddings.
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
To this end, considering the crucial role of the body parts in the spatially concentrated human actions, we attend to the mixing augmentations and propose a novel method, Shap-Mix, which improves long-tailed learning by mining representative motion patterns for tail categories.
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
TaatiTeam/STARS
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Self-supervised pretraining methods with masked prediction demonstrate remarkable within-dataset performance in skeleton-based action recognition.
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks
pablopardod/dyalyt
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IEEE Intelligent Vehicles Symposium (IV) 2024
Our results highlight the effectiveness and robustness of speech recognition networks in pose-based action classification.
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively.
