Hand Tracking with an Extended Self-Organizing Map

Andreea State, Foti Coleca, Erhardt Barth, Thomas Martinetz


We introduce an extension of the self-organizing map for performing 3D hand skeleton tracking. We use a range camera for data acquisition and apply a SOM-like learning process within each frame in order to capture the hand pose. Our method uses a topology consisting of 1D and 2D segments for an improved representation of the hand. The proposed algorithm is very efficient and produces good tracking results.

Original languageEnglish
Title of host publicationAdvances in Self-Organizing Maps
EditorsPablo A. Estévez, José C. Príncipe, Pablo Zegers
Number of pages10
VolumeVol. 198
PublisherSpringer Berlin Heidelberg
Publication date2013
ISBN (Print)978-3-642-35229-4
ISBN (Electronic)978-3-642-35230-0
Publication statusPublished - 2013
Event9th Workshop on Self-Organizing Maps - Santiago, Santiago, Chile
Duration: 12.12.201214.12.2012


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