SOTAVerified

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 411419 of 419 papers

TitleStatusHype
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition0
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks0
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition0
Modeling Video Evolution for Action Recognition0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation0
Motion feature augmented network for dynamic hand gesture recognition from skeletal data0
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition0
Show:102550
← PrevPage 42 of 42Next →

No leaderboard results yet.