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Skeleton Based Action Recognition

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 )

Papers

Showing 211220 of 419 papers

TitleStatusHype
STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action RecognitionCode0
Structure-Aware Convolutional Neural NetworksCode0
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action RecognitionCode0
Temporal-Channel Topology Enhanced Network for Skeleton-Based Action RecognitionCode0
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action RecognitionCode0
Tracking Emerges by Colorizing VideosCode0
UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildCode0
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints InvarianceCode0
Unsupervised Motion Representation Learning with Capsule AutoencodersCode0
View Adaptive Neural Networks for High Performance Skeleton-based Human Action RecognitionCode0
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