Paper Info
Title
Multi-User Egocentric Online System For Unsupervised Assistance On Object Usage
Abstract
We present an online fully unsupervised approach for automatically extracting video guides of how objects are used from wearable gaze trackers worn by multiple users. Given egocentric video and eye gaze from multiple users performing tasks, the system discovers task-relevant objects and automatically extracts guidance videos on how these objects have been used. In the assistive mode, 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 approach is tested on a variety of daily tasks ranging from opening a door, to preparing coffee and operating a gym machine.
Year
DOI
Venue
2014
10.1007/978-3-319-16199-0_34
COMPUTER VISION - ECCV 2014 WORKSHOPS, PT III
Keywords
Field
DocType
Video guidance, Wearable computing, Real-time computer vision, Assistive computing, Object discovery, Object usage
Computer vision,BitTorrent tracker,Gaze,Computer science,Wearable computer,Eye tracking,Ranging,Artificial intelligence,Multi-user
Conference
Volume
ISSN
Citations 
8927
0302-9743
4
PageRank 
References 
Authors
0.42
14
5
Name
Order
Citations
PageRank
Dima Damen122531.54
Osian Haines2443.46
Teesid Leelasawassuk3344.16
Andrew Calway464554.66
Walterio W. Mayol-cuevas549748.81