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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 391–400 of 419 papers

TitleStatusHype
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton DataCode0
A New Representation of Skeleton Sequences for 3D Action Recognition—0
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM NetworksCode0
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition—0
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks—0
Deep Learning on Lie Groups for Skeleton-based Action Recognition—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep NetworksCode0
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data—0
Multi-region two-stream R-CNN for action detection—0
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