Abstract
Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.
This work has been supported by the Spanish Government, the Canary Islands Autonomous Government and the Univ. of Las Palmas de G.C. under projects TIN2004-07087, PI20003/165 and UNI2003/06.
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Antón-Canalís, L., Sánchez-Nielsen, E., Castrillón-Santana, M. (2005). Fast and Accurate Hand Pose Detection for Human-Robot Interaction. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_67
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DOI: https://doi.org/10.1007/11492429_67
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