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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 311320 of 419 papers

TitleStatusHype
Skeleton-based action analysis for ADHD diagnosis0
Skeleton-based Action Recognition of People Handling Objects0
Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks0
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates0
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn0
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information0
Skeleton-based Action Recognition Using LSTM and CNN0
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method0
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling0
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention0
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