This paper presents an unsupervised approach towards automatically extractingvideo-based guidance on object usage, from egocentric video and wearable gazetracking, collected from multiple users while performing tasks. The approachi) 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 thedependencies 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.
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