Abstract
Touch-free gesture technology opens new avenues for human-machine interaction. We show how self-organizing maps (SOM) can be used for hand and full body tracking. We use a range camera for data acquisition and apply a SOM-learning process for each frame in order to capture the pose. In a next step we introduce an extension of the SOM to 1D and 2D segments for an improved representation and skeleton tracking of body and hand. The proposed SOM based algorithms are very efficient and robust, and produce good tracking results. Their efficiency allows to implement these algorithms on embedded systems, which we demonstrate on an ARM-based embedded platform.
| Original language | English |
|---|---|
| Journal | Neurocomputing |
| Volume | 147 |
| Issue number | 1 |
| Pages (from-to) | 174-184 |
| Number of pages | 11 |
| ISSN | 0925-2312 |
| DOIs | |
| Publication status | Published - 01.01.2015 |
| Event | 9th Workshop on Self-Organizing Maps - Santiago, Santiago, Chile Duration: 12.12.2012 → 14.12.2012 http://www.die.uchile.cl/wsom2012/ |
Funding
This research is supported by the German Ministry of Education and Research (BMBF) under grant number 01IS10049B , the EXIST program of the German Ministry of Economics and Technology (BMWi) , and by the Graduate School for Computing in Medicine and Life Sciences funded by Germany׳s Excellence Initiative [DFG GSC 235/1]. A. State would like to thank for the support by The German Academic Exchange Service Programme “Ostpartnerschaften”, the University of Lübeck and The Sectoral Operational Programme Human Resources Development 2007–2013 of the Romanian Ministry of Labor, Family and Social Protection through the Financial Agreement POSDRU/86/1.2/S/61756.
