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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

Showing 301310 of 419 papers

TitleStatusHype
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition0
The Multi-Modal Video Reasoning and Analyzing Competition0
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition0
Totally Deep Support Vector Machines0
Towards a Skeleton-Based Action Recognition For Realistic Scenarios0
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition0
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition0
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling0
Unsupervised representation learning with long-term dynamics for skeleton based action recognition0
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