Left/right hand segmentation in egocentric videos

A. Betancourt Arango, P. Morerio, E.I. Barakova, L. Marcenaro, G.W.M. Rauterberg, C.S. Regazzoni

    Research output: Contribution to journalArticleAcademicpeer-review

    11 Citations (Scopus)
    4 Downloads (Pure)


    Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras 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 handle hand segmentation as a background-foreground problem, ignoring two important facts: i) hands are not a single “skin-like” moving element, but a pair of interacting cooperative entities, ii) close hand interactions may lead to hand-to-hand occlusions and, as a consequence, create a single hand-like segment. These facts complicate a proper understanding of hand movements and interactions. Our approach extends traditional background-foreground strategies, by including a hand-identification step (left-right) based on a Maxwell distribution of angle and position. Hand-to-hand occlusions are addressed by exploiting temporal superpixels. The experimental results show that, in addition to a reliable left/right hand-segmentation, our approach considerably improves the traditional background-foreground hand-segmentation.
    Original languageEnglish
    Pages (from-to)73–81
    Number of pages9
    JournalComputer Vision and Image Understanding
    Early online date2016
    Publication statusPublished - Jan 2017


    • Egocentric vision
    • First person vision
    • Hand-identification
    • Hand-segmentation


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