Graph neural network: Difference between revisions

Revision as of 06:26, 6 April 2025

Revision as of 08:37, 6 April 2025

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=== Computer Vision ===

Moving objects detection in video surveillance is a challenging task in computer vision. Graph neural networks allow to separate background and foreground objects
<ref name="Prummel">{{cite journal|title=Graph Neural Networks for Moving Object Detection in Videos|journal=Pattern Recognition and Computer Vision in the New AI Era, Edited by C.H. Chen, World Scientific Publishing|date=April 2025|last1=Prummel|first1=W.|last2=Giraldo|first2=H.|last3=Zakharova|first3=A.|last4=Bouwmans|first4=T.}}</ref> . Transductive <ref name="Giraldo">{{cite journal|title=Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos|journal=Fourth Workshop on Robust Subspace Learning and Computer Vision, ICCV 2021|date=October 2021|url=https://ieeexplore.ieee.org/document/9607835|last1=Giraldo|first1=H.|last2=Javed|first2=S.|last3=Werghi|first3=N.|last4=Bouwmans|first4=T.}}</ref> and inductive GNNs <ref name="PrummelPrummel1">{{cite journal|title=Inductive Graph Neural Networks for Moving Object Segmentation|journal=IEEE International Conference on Image Processing, ICIP 2023|date=October 2023|url=https://arxiv.org/abs/2305.09585|last1=Prummel|first1=W.|last2=Giraldo|first2=H.|last3=Zakharova|first3=A.|last4=Bouwmans|first4=T.}}</ref>

have shown interesting performance to detect moving objects in urban environments and natural environments.

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