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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 381–390 of 419 papers

TitleStatusHype
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action RecognitionCode0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates—0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition—0
Investigation of Different Skeleton Features for CNN-based 3D Action RecognitionCode0
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn—0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and DetectionCode0
Skeletonnet: Mining deep part features for 3-d action recognition—0
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