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
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition
WoominM/DeGCN_pytorch
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IEEE Transactions on Image Processing 2024
Graph convolutional networks (GCN) have recently been studied to exploit the graph topology of the human body for skeleton-based action recognition.
GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
deepintostreams/gcn-devlstm
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Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision.
Skeleton-Based Human Action Recognition with Noisy Labels
In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark.
SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
We categorize the key skeletal-temporal relations for action recognition into a total of four distinct types.
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search
This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs).
Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence
In this paper, we show that using high-level contextualized features as prediction targets can achieve superior performance.
Spatial-Temporal Decoupling Contrastive Learning for Skeleton-based Human Action Recognition
Furthermore, to explicitly exploit the latent data distributions, we employ the attentive features to contrastive learning, which models the cross-sequence semantic relations by pulling together the features from the positive pairs and pushing away the negative pairs.
Navigating Open Set Scenarios for Skeleton-based Action Recognition
kpeng9510/os-sar
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In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones.
STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action Recognition
We think the key to skeleton-based action recognition is a skeleton hanging in frames, so we focus on how the Graph Convolutional Convolution networks learn different topologies and effectively aggregate joint features in the global temporal and local temporal.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
