3D Finger CAPE: Clicking Action and Position Estimation under Self-Occlusions in Egocentric Viewpoint

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In this paper we present a novel framework for simultaneous detection of click action and estimation of occluded fingertip positions from egocentric viewed single-depth image sequences. For the detection and estimation, a novel probabilistic inference based on knowledge priors of clicking motion and clicked position is presented. Based on the detection and estimation results, we were able to achieve a fine resolution level of a bare hand-based interaction with virtual objects in egocentric viewpoint. Our contributions include: (i) a rotation and translation invariant finger clicking action and position estimation using the combination of 2D image-based fingertip detection with 3D hand posture estimation in egocentric viewpoint. (ii) a novel spatio-temporal random forest, which performs the detection and estimation efficiently in a single framework. We also present (iii) a selection process utilizing the proposed clicking action detection and position estimation in an arm reachable AR/VR space, which does not require any additional device. Experimental results show that the proposed method delivers promising performance under frequent self-occlusions in the process of selecting objects in AR/VR space whilst wearing an egocentric-depth camera-attached HMD.
Publisher
IEEE COMPUTER SOC
Issue Date
2015-04
Language
English
Article Type
Article; Proceedings Paper
Citation

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.21, no.4, pp.501 - 510

ISSN
1077-2626
DOI
10.1109/TVCG.2015.2391860
URI
http://hdl.handle.net/10203/198310
Appears in Collection
GCT-Journal Papers(저널논문)
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