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
Most implemented papers
Temporal Convolutional Networks for Action Segmentation and Detection
colincsl/TemporalConvolutionalNetworks
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CVPR 2017
The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
mdeff/cnn_graph
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NeurIPS 2016
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
benedekrozemberczki/pytorch_geometric_temporal
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CVPR 2019
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
kenziyuliu/ms-g3d
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CVPR 2020
Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics.
Revisiting Skeleton-based Action Recognition
open-mmlab/mmaction2
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CVPR 2022
In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints.
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
To enhance the robustness of action recognition models to incomplete skeletons, we propose a multi-stream graph convolutional network (GCN) for exploring sufficient discriminative features distributed over all skeleton joints.
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed.
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
Skeleton Aware Multi-modal Sign Language Recognition
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master.
