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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 181–190 of 419 papers

TitleStatusHype
Modeling Video Evolution for Action Recognition—0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
Motion feature augmented network for dynamic hand gesture recognition from skeletal data—0
Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition—0
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition—0
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition—0
Learning Human Activities and Object Affordances from RGB-D Videos—0
An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition—0
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