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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 331340 of 419 papers

TitleStatusHype
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation0
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention0
DeepGRU: Deep Gesture Recognition UtilityCode0
Part-based Graph Convolutional Network for Action RecognitionCode0
Neural Graph Matching Networks for Fewshot 3D Action Recognition0
Early action prediction by soft regression0
Adding Attentiveness to the Neurons in Recurrent Neural Networks0
Tracking Emerges by Colorizing VideosCode0
Pose Encoding for Robust Skeleton-Based Action Recognition0
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