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
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.
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art
Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis.
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
stnoah1/sode
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To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition.
Unveiling the Hidden Realm: Self-supervised Skeleton-based Action Recognition in Occluded Environments
cyfml/opstl
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To integrate action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions.
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i. e., joints, bones, and motions are used, hence no additional modalities are explored.
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition
In order to solve this dilemma, we propose a multi-semantic fusion (MSF) model for improving the performance of GZSSAR, where two kinds of class-level textual descriptions (i. e., action descriptions and motion descriptions), are collected as auxiliary semantic information to enhance the learning efficacy of generalizable skeleton features.
SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition
Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition.
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action Recognition
gftww/B2C
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IEEE Transactions on Image Processing 2023
Human Action Recognition plays a driving engine of many human-computer interaction applications.
Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition
firework8/brl
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Secondly, we design a detached action-aware learning schedule to further mitigate the bias in the representation space.
Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition
We propose a method specifically designed for hand action recognition which uses relative angular embeddings and local Spherical Harmonics to create novel hand representations.
