A hand pose tracking benchmark from stereo matching

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE
Pages982-986
ISBN (Electronic)978-1-5090-2175-8
Publication statusPublished - Sep 2017

Conference

Title24th IEEE International Conference on Image Processing, ICIP 2017
LocationChina National Convention Center
PlaceChina
CityBeijing
Period17 - 20 September 2017

Abstract

In this paper we establish a long-term 3D hand pose tracking benchmark1. It contains 18,000 stereo image pairs as well as the ground-truth 3D positions of palm and finger joints from different scenarios. Meanwhile, to accurately segment hand from stereo images, we propose a novel stereo-based hand segmentation and depth estimation algorithm specially tailored for hand tracking here. The experiments indicate the effectiveness of the proposed algorithm by demonstrating that its tracking performance is comparable to the use of an active depth sensor under various of challenging scenarios.

Research Area(s)

  • Stereo matching and hand pose tracking

Citation Format(s)

A hand pose tracking benchmark from stereo matching. / Zhang, Jiawei; Jiao, Jianbo; Chen, Mingliang; Qu, Liangqiong; Xu, Xiaobin; Yang, Qingxiong.

2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE, 2017. p. 982-986.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review