A data-set and a method for pointing direction estimation from depth images for human-robot interaction and VR applications

Das, SS (2021) A data-set and a method for pointing direction estimation from depth images for human-robot interaction and VR applications. In: 2021 IEEE International Conference on Robotics and Automation, 30 May - 5 Jun 2021, Xi'an, pp. 11485-11491.

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Abstract

3D pointing devices are indispensable in virtual reality (hereafter VR) and human-robot interaction scenarios. Existing devices are cumbersome or non-immersive or have a limited volume of operation. Hand gesture-based interfaces do not suffer from these problems and can be used for 3D pointing purposes. However, there is a lack of robust, accurate hand gesture-based pointing techniques which can be attributed to the non-existence of large and accurate data-set for the same. To overcome this barrier, we propose a data-set consisting of depth images with a large number (107000) of samples collected from 11 subjects, with accurate ground-truth and adequate variation in the orientation and distance of the hand w.r.t. the camera. We propose a 3D convolutional neural network based technique that works on the proposed data-set and achieves an accuracy of 94.49 for an angle error threshold of 10 degrees. The proposed data-set may be used for developing more accurate, robust, less computationally expensive methods. © 2021 IEEE

Item Type:	Conference Paper
Publication:	Proceedings - IEEE International Conference on Robotics and Automation
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	18 Mar 2022 11:58
Last Modified:	18 Mar 2022 11:58
URI:	http://eprints.iisc.ac.in/id/eprint/71603

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