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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 301–310 of 419 papers

TitleStatusHype
Bootstrapped Representation Learning for Skeleton-Based Action Recognition—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision—0
BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation—0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition—0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition—0
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve—0
Skeletal quads: Human action recognition using joint quadruples—0
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks—0
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