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Title:
Diagnosing Error in Object Detectors | SpringerLink
Description:
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,...
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Keywords {π}
google, scholar, object, detection, cvpr, computer, eccv, part, recognition, chapter, research, paper, error, detectors, science, zisserman, visual, privacy, cookies, analysis, information, publish, vision, conference, hoiem, similar, objects, perona, institute, springer, heidelberg, content, search, classes, vedaldi, detector, felzenszwalb, iccv, ramanan, models, schmid, article, data, journal, chodpathumwan, dai, localization, multiple, study, download,
Topics {βοΈ}
org/challenges/voc/voc2007/workshop/index technical report cmu-ri-tr-01-02 hog-lbp human detector /~pff/latent-release4/ park technical report cns-tr-2010-001 multi-view probabilistic model fine-grained image categorization flexible object models diagnosing error 3d object classes localization error privacy choices/manage cookies perform similar analysis context based recognition semantically similar objects deformable part model layered object detection object detection applied learning algorithm improves main content log future researchers multi-class segmentation visual scene understanding edgelet part detectors caltech-ucsd birds 200 recognition research achieve large gains partially occluded humans structured output regression interest point detectors plos computational biologyΒ 4 conditions privacy policy paper cite european economic area accepting optional cookies object detectors similar objects nsf awards iis-1053768 discriminatively trained detailed analysis essential european conference partial occlusion handling object detection computer science technical report 7694 conference series objects classification approaches journal finder publish machine vision object characteristics
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Schema {πΊοΈ}
ScholarlyArticle:
headline:Diagnosing Error in Object Detectors
pageEnd:353
pageStart:340
image:https://media.springernature.com/w153/springer-static/cover/book/978-3-642-33712-3.jpg
genre:
Computer Science
Computer Science (R0)
isPartOf:
name:Computer Vision β ECCV 2012
isbn:
978-3-642-33712-3
978-3-642-33711-6
type:Book
publisher:
name:Springer Berlin Heidelberg
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
type:Organization
author:
name:Derek Hoiem
affiliation:
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
type:Organization
type:Person
name:Yodsawalai Chodpathumwan
affiliation:
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
type:Organization
type:Person
name:Qieyun Dai
affiliation:
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
type:Organization
type:Person
keywords:Localization Error, Object Detector, Average Precision, Object Category, Similar Object
description: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.
datePublished:2012
isAccessibleForFree:1
context:https://schema.org
Book:
name:Computer Vision β ECCV 2012
isbn:
978-3-642-33712-3
978-3-642-33711-6
Organization:
name:Springer Berlin Heidelberg
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
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url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
name:Derek Hoiem
affiliation:
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
type:Organization
name:Yodsawalai Chodpathumwan
affiliation:
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
type:Organization
name:Qieyun Dai
affiliation:
name:University of Illinois at Urbana-Champaign
address:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
type:PostalAddress
type:Organization
PostalAddress:
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
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