You-Do, I-Learn: Egocentric Unsupervised Discovery of Objects and their Modes of Interaction Towards Video-Based Guidance

Dima Damen, Teesid Leelasawassuk, Walterio W Mayol-Cuevas

Research output: Contribution to journalSpecial issue (Academic Journal)peer-review

24 Citations (Scopus)
459 Downloads (Pure)

Abstract

This paper presents an unsupervised approach towards automatically extracting
video-based guidance on object usage, from egocentric video and wearable gaze
tracking, collected from multiple users while performing tasks. The approach
i) discovers task relevant objects, ii) builds a model for each, iii) distinguishes different ways in which each discovered object has been used and vi) discovers the
dependencies between object interactions. The work investigates using appearance, position, motion and attention, and presents results using each and a combination of relevant features. Moreover, an online scalable approach is presented and is compared to offline results. The paper proposes a method for selecting a suitable video guide to be displayed to a novice user indicating how to use an object, purely triggered by the user’s gaze. The potential assistive mode can also recommend an object to be used next based on the learnt sequence of object interactions. The approach was tested on a variety of daily tasks such as initialising a printer, preparing a coffee and setting up a gym machine.
Original languageEnglish
Pages (from-to)98-112
Number of pages15
JournalComputer Vision and Image Understanding
Volume149
Early online date7 Jun 2016
DOIs
Publication statusPublished - Aug 2016

Keywords

  • Video Guidance
  • Real-time Computer Vision
  • Assistive Computing
  • Object Discovery
  • Object Usage

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