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Ashish Doshi, Jonathan Starck, and Adrian Hilton, An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video. JVRB - Journal of Virtual Reality and Broadcasting, 7(2010), no. 3. (urn:nbn:de:0009-6-25740)

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%0 Journal Article
%T An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video
%A Doshi, Ashish
%A Starck, Jonathan
%A Hilton, Adrian
%J JVRB - Journal of Virtual Reality and Broadcasting
%D 2010
%V 7(2010)
%N 3
%@ 1860-2037
%F doshi2010
%X This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people   with variation in clothing and movement. This analysis  identifies that existing feature detection and matching  algorithms are unreliable for fast movement with common  clothing.
%L 004
%K Feature Matching
%K Qualitative Analysis
%K Recall-Accuracy
%K Sift
%K Video Sequences
%R 10.20385/1860-2037/7.2010.3
%U http://nbn-resolving.de/urn:nbn:de:0009-6-25740
%U http://dx.doi.org/10.20385/1860-2037/7.2010.3
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Bibtex

@Article{doshi2010,
  author = 	"Doshi, Ashish
		and Starck, Jonathan
		and Hilton, Adrian",
  title = 	"An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video",
  journal = 	"JVRB - Journal of Virtual Reality and Broadcasting",
  year = 	"2010",
  volume = 	"7(2010)",
  number = 	"3",
  keywords = 	"Feature Matching; Qualitative Analysis; Recall-Accuracy; Sift; Video Sequences",
  abstract = 	"This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people   with variation in clothing and movement. This analysis  identifies that existing feature detection and matching  algorithms are unreliable for fast movement with common  clothing.",
  issn = 	"1860-2037",
  doi = 	"10.20385/1860-2037/7.2010.3",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-6-25740"
}
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RIS

TY  - JOUR
AU  - Doshi, Ashish
AU  - Starck, Jonathan
AU  - Hilton, Adrian
PY  - 2010
DA  - 2010//
TI  - An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video
JO  - JVRB - Journal of Virtual Reality and Broadcasting
VL  - 7(2010)
IS  - 3
KW  - Feature Matching
KW  - Qualitative Analysis
KW  - Recall-Accuracy
KW  - Sift
KW  - Video Sequences
AB  - This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people   with variation in clothing and movement. This analysis  identifies that existing feature detection and matching  algorithms are unreliable for fast movement with common  clothing.
SN  - 1860-2037
UR  - http://nbn-resolving.de/urn:nbn:de:0009-6-25740
DO  - 10.20385/1860-2037/7.2010.3
ID  - doshi2010
ER  -
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Wordbib

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<b:Comments>This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people   with variation in clothing and movement. This analysis  identifies that existing feature detection and matching  algorithms are unreliable for fast movement with common  clothing.</b:Comments>
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ISI

PT Journal
AU Doshi, A
   Starck, J
   Hilton, A
TI An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video
SO JVRB - Journal of Virtual Reality and Broadcasting
PY 2010
VL 7(2010)
IS 3
DI 10.20385/1860-2037/7.2010.3
DE Feature Matching; Qualitative Analysis; Recall-Accuracy; Sift; Video Sequences
AB This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people   with variation in clothing and movement. This analysis  identifies that existing feature detection and matching  algorithms are unreliable for fast movement with common  clothing.
ER
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Mods

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  <titleInfo>
    <title>An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video</title>
  </titleInfo>
  <name type="personal">
    <namePart type="family">Doshi</namePart>
    <namePart type="given">Ashish</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Starck</namePart>
    <namePart type="given">Jonathan</namePart>
  </name>
  <name type="personal">
    <namePart type="family">Hilton</namePart>
    <namePart type="given">Adrian</namePart>
  </name>
  <abstract>This paper presents an empirical study of affine
invariant feature detectors to perform matching on
video sequences of people with non-rigid surface
deformation. Recent advances in feature detection and
wide baseline matching have focused on static scenes.
Video frames of human movement capture highly
non-rigid deformation such as loose hair, cloth creases,
skin stretching and free flowing clothing. This study
evaluates the performance of six widely used feature
detectors for sparse temporal correspondence on single
view and multiple view video sequences. Quantitative
evaluation is performed of both the number of features
detected and their temporal matching against and without
ground truth correspondence. Recall-accuracy analysis of
feature matching is reported for temporal correspondence
on single view and multiple view sequences of people   with variation in clothing and movement. This analysis
  identifies that existing feature detection and matching
  algorithms are unreliable for fast movement with common
  clothing.</abstract>
  <subject>
    <topic>Feature Matching</topic>
    <topic>Qualitative Analysis</topic>
    <topic>Recall-Accuracy</topic>
    <topic>Sift</topic>
    <topic>Video Sequences</topic>
  </subject>
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  <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-6-25740</identifier>
  <identifier type="citekey">doshi2010</identifier>
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Full Metadata

Bibliographic Citation JVRB, 7(2010), no. 3.
Title

An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video (eng)
Author Ashish Doshi, Jonathan Starck, Adrian Hilton
Language eng
Abstract This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.
Subject Feature Matching, Qualitative Analysis, Recall-Accuracy, Sift, Video Sequences
Classified Subjects
swd
  • 4145413-3, Bildsequenz
  • 4536854-5, Videobearbeitung
DDC 004
Rights DPPL
URN: urn:nbn:de:0009-6-25740
DOI https://doi.org/10.20385/1860-2037/7.2010.3