Dynamic learning, retrieval, and tracking to augment hundreds of photographs

Julien Pilet, Hideo Saito

研究成果: Article査読

抄録

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 cannot 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. Dynamic learning of new viewpoints as the camera moves naturally yields the kind of robustness and reliability expected from an augmented reality engine. 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. We released the source code of our system in a package called Polyora.

本文言語English
ページ(範囲)89-100
ページ数12
ジャーナルVirtual Reality
18
2
DOI
出版ステータスPublished - 2014 6月

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ グラフィックスおよびコンピュータ支援設計

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