Diagnosing Error in Object Detectors View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Derek Hoiem , Yodsawalai Chodpathumwan , Qieyun Dai

ABSTRACT

This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of false positives. In particular, we examine effects of occlusion, size, aspect ratio, visibility of parts, viewpoint, localization error, and confusion with semantically similar objects, other labeled objects, and background. We analyze two classes of detectors: the Vedaldi et al. multiple kernel learning detector and different versions of the Felzenszwalb et al. detector. Our study shows that sensitivity to size, localization error, and confusion with similar objects are the most impactful forms of error. Our analysis also reveals that many different kinds of improvement are necessary to achieve large gains, making more detailed analysis essential for the progress of recognition research. By making our software and annotations available, we make it effortless for future researchers to perform similar analysis. More... »

PAGES

340-353

References to SciGraph publications

  • 2000-06. Evaluation of Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010-10. A comparative evaluation of interest point detectors and local descriptors for visual SLAM in MACHINE VISION AND APPLICATIONS
  • 2010. Multiresolution Models for Object Detection in COMPUTER VISION – ECCV 2010
  • 2008. Searching the World’s Herbaria: A System for Visual Identification of Plant Species in COMPUTER VISION – ECCV 2008
  • 2010-06. The Pascal Visual Object Classes (VOC) Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Computer Vision – ECCV 2012

    ISBN

    978-3-642-33711-6
    978-3-642-33712-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-33712-3_25

    DOI

    http://dx.doi.org/10.1007/978-3-642-33712-3_25

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1027572846


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