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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 91100 of 419 papers

TitleStatusHype
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action RecognitionCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
Traffic Control Gesture Recognition for Autonomous VehiclesCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
VPN: Learning Video-Pose Embedding for Activities of Daily LivingCode1
Quo Vadis, Skeleton Action Recognition ?Code1
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