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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

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Showing 231–240 of 419 papers

TitleStatusHype
Modeling Video Evolution for Action Recognition—0
Motion feature augmented network for dynamic hand gesture recognition from skeletal data—0
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition—0
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition—0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition—0
Multi-region two-stream R-CNN for action detection—0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition—0
Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
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