Virtually augmenting hundreds of real pictures: An approach based on learning, retrieval, and tracking

Julien Pilet, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

Abstract

Tracking is a major issue of virtual and augmented reality applications. Single object tracking on monocular video streams is fairly well understood. However, when it comes to multiple objects, existing methods lack scalability and can recognize only a limited number of objects. Thanks to recent progress in feature matching, state-of-the-art image retrieval techniques can deal with millions of images. However, these methods do not focus on real-time video processing and can not track retrieved objects. In this paper, we present a method that combines the speed and accuracy of tracking with the scalability of image retrieval. At the heart of our approach is a bi-layer clustering process that allows our system to index and retrieve objects based on tracks of features, thereby effectively summarizing the information available on multiple video frames. As a result, our system is able to track in real-time multiple objects, recognized with low delay from a database of more than 300 entries.

Original languageEnglish
Title of host publicationProceedings - IEEE Virtual Reality
Pages71-78
Number of pages8
DOIs
Publication statusPublished - 2010
EventIEEE Virtual Reality 2010, VR 2010 - Waltham, MA, United States
Duration: 2010 Mar 202010 Mar 24

Other

OtherIEEE Virtual Reality 2010, VR 2010
CountryUnited States
CityWaltham, MA
Period10/3/2010/3/24

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ASJC Scopus subject areas

  • Engineering(all)

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