Towards Multi-View Object Class Detection

A. Thomas, Vittorio Ferrari, B. Leibe, T. Tuytelaars, B. Schiel, L. Van Gool

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

Abstract / Description of output

We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class detection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al. After learning single-view codebooks, these are interconnected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these integrated codebooks work together to determine the location and pose of the object. Experimental results demonstrate the viability of the approach and compare it to a bank of independent single-view detectors.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)0-7695-2597-0
Publication statusPublished - 2006

Keywords / Materials (for Non-textual outputs)

  • Airplanes
  • Detectors
  • Image recognition
  • Joining processes
  • Object detection
  • Object recognition
  • Power system modeling
  • Shape
  • Testing
  • Voting


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