You-Do, I-Learn: Unsupervised Multi-User egocentric Approach Towards Video-Based Guidance
This work addresses the need for assistive guidance in daily tasks for novice users, though it appears incremental as it builds on existing unsupervised and feature-based methods.
The paper tackles the problem of automatically extracting video-based guidance for object usage from unsupervised multi-user egocentric video and gaze data, achieving results that include discovering task-relevant objects, modeling their usage, and learning dependencies between object interactions.
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 iv) 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.