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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 411419 of 419 papers

TitleStatusHype
Finding Action TubesCode0
10,000+ Times Accelerated Robust Subset Selection (ARSS)0
Skeletal quads: Human action recognition using joint quadruples0
Large-Scale Video Classification with Convolutional Neural NetworksCode1
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation0
UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildCode0
Learning Human Activities and Object Affordances from RGB-D Videos0
View invariant human action recognition using histograms of 3D joints0
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