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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 2130 of 419 papers

TitleStatusHype
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton SequencesCode1
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNCode1
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action RecognitionCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
Taylor Videos for Action RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
Navigating Open Set Scenarios for Skeleton-based Action RecognitionCode1
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
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