A Mobile Vision System for Urban Object Detection with Informative Local Descriptors

Gerald Fritz, Christin Seifert, Lucas Paletta

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

86 Citations (Scopus)
226 Downloads (Pure)

Abstract

We present a computer vision system for the detection and identification of urban objects from mobile phone imagery, e.g., for the application of tourist information services. Recognition is based on MAP decision making over weak object hypotheses from local descriptor responses in the mobile imagery. We present an improvement over the standard SIFT key detector [7] by selecting only informative (i-SIFT) keys for descriptor matching. Selection is applied first to reduce the complexity of the object model and second to accelerate detection by selective filtering. We present results on the MPG-20 mobile phone imagery with severe illumination, scale and viewpoint changes in the images, performing with 98% accuracy in identification, efficient (100 background rejection, efficient (0 false alarm rate, and reliable quality of service under extreme illumination conditions, significantly improving standard SIFT based recognition in every sense, providing (important for mobile vision) runtimes which are 8 ( 24) times faster for the MPG-20 (ZuBuD) database.
Original languageEnglish
Title of host publicationProceedings of the 4th IEEE International Conference on Computer Vision Systems (ICVS 2006)
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Print)0-7695-2506-7
DOIs
Publication statusPublished - 1 Jan 2006
Externally publishedYes
Event4th IEEE International Conference on Computer Vision Systems, ICVS 2006 - New York, United States
Duration: 4 Jan 20066 Jan 2006
Conference number: 4

Conference

Conference4th IEEE International Conference on Computer Vision Systems, ICVS 2006
Abbreviated titleICVS
Country/TerritoryUnited States
CityNew York
Period4/01/066/01/06

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