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3D Hand Gesture Analysis Through a Real-time Gesture Search Engine
KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.
KTH, Skolan för datavetenskap och kommunikation (CSC), Medieteknik och interaktionsdesign, MID.ORCID-id: 0000-0003-3779-5647
2015 (engelsk)Inngår i: International Journal of Advanced Robotic Systems, ISSN 1729-8806, E-ISSN 1729-8814, Vol. 12, artikkel-id 67Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

3D gesture recognition and tracking are highly desired features of interaction design in future mobile and smart environments. Specifically, in virtual/augmented reality applications, intuitive interaction with the physical space seems unavoidable and 3D gestural interaction might be the most effective alternative for the current input facilities such as touchscreens. In this paper, we introduce a novel solution for real-time 3D gesture-based interaction by finding the best match from an extremely large gesture database. This database includes images of various articulated hand gestures with the annotated 3D position/orientation parameters of the hand joints. Our unique matching algorithm is based on the hierarchical scoring of the low-level edge-orientation features between the query frames and database and retrieving the best match. Once the best match is found from the database in each moment, the pre-recorded 3D motion parameters can instantly be used for natural interaction. The proposed bare-hand interaction technology performs in real time with high accuracy using an ordinary camera.

sted, utgiver, år, opplag, sider
2015. Vol. 12, artikkel-id 67
Emneord [en]
Gesture Recognition, Gesture Tracking, 3D Motion Analysis, Gesture Search Engine, 3D Interaction
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-172499DOI: 10.5772/60045ISI: 000357672200001Scopus ID: 2-s2.0-84934917557OAI: oai:DiVA.org:kth-172499DiVA, id: diva2:848577
Merknad

QC 20150825

Tilgjengelig fra: 2015-08-25 Laget: 2015-08-25 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Yousefi, ShahrouzLi, Haibo
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