GPU accelerated left/right hand-segmentation in first person vision

A. Betancourt Arango, L. Marcenaro, E.I. Barakova, M. Rauterberg, C.S. Regazzoni

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Wearable cameras allow users to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favourable location, they frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision, methods understand the hands as a background/foreground segmentation problem that ignores two important issues: (i) Each pixel is sequentially classified creating a long processing queue, (ii) Hands are not a single “skin-like” moving element but a pair of interacting entities (left-right hand). This paper proposes a GPU-accelerated implementation of a left right-hand segmentation algorithm. The GPU implementation exploits the nature of the pixel-by-pixel classification strategy. The left-right identification is carried out by following a competitive likelihood test based the position and the angle of the segmented pixels.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2016 Workshops, Proceedings, 8-10/15-16 October 2016, Amsterdam, The Netherlands
EditorsG. Hua, H. Jegou
Place of PublicationDordrecht
PublisherSpringer
Pages504-517
Number of pages14
ISBN (Electronic)978-3-319-46604-0
ISBN (Print)9783319466033
DOIs
Publication statusPublished - 2016
Event14th European Conference on Computer Vision (ECCV 2016) - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016
Conference number: 14

Publication series

NameLecture Notes in Computer Science
Volume9913
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference14th European Conference on Computer Vision (ECCV 2016)
Abbreviated titleECCV 2016
Country/TerritoryNetherlands
CityAmsterdam
Period8/10/1616/10/16

Keywords

  • Egovision
  • GPU
  • Hand-detection
  • Hand-segmentation
  • Wearable cameras

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