Centroid-based graph matching networks for planar object tracking

Recently, keypoint-based methods have received more attention on planar object tracking due to their abilities to deal with partial noises, such as occlusion and out-of-view. However, robust tracking is still a tricky problem in the case of fast movement, large transformation and motion blur. The ke...

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Vydané v:	Machine vision and applications Ročník 34; číslo 2; s. 31
Hlavní autori:	Li, Kunpeng, Liu, He, Wang, Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023 Springer Nature B.V
Predmet:	Algorithms Blurring Centroids Communications Engineering Computer Science Datasets Deep learning Design Graph matching Image Processing and Computer Vision Inliers (landforms) Localization Methods Movement Networks Occlusion Original Paper Pattern Recognition Perturbation Template matching Tracking Transformations Vision systems Planar object tracking Deep learning Centroid Pose estimation Keypoint Graph matching
ISSN:	0932-8092, 1432-1769
On-line prístup:	Získať plný text
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