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
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
charlesq34/pointnet
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CVPR 2017
Point cloud is an important type of geometric data structure.
Graph Attention Networks
PetarV-/GAT
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ICLR 2018
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Semi-Supervised Classification with Graph Convolutional Networks
tkipf/pygcn
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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
open-mmlab/mmaction2
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CVPR 2017
The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks.
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
yysijie/st-gcn
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Dynamics of human body skeletons convey significant information for human action recognition.
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
NVIDIA/flownet2-pytorch
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CVPR 2017
Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Sunnydreamrain/IndRNN_Theano_Lasagne
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CVPR 2018
Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
Simplifying Graph Convolutional Networks
Tiiiger/SGC
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Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
hikvision-research/skelact
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Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets.
