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Pedestrian Detection Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs CVPR ‘05

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Pedestrian Detection Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs CVPR ‘05. Pete Barnum March 8, 2006. Challenges. Wide variety of articulated poses Variable appearance/clothing Complex backgrounds Unconstrined illumination Occlusions

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Pedestrian DetectionHistograms of Oriented Gradients for Human DetectionNavneet Dalal and Bill Triggs CVPR ‘05

Pete Barnum

March 8, 2006

challenges
Challenges
  • Wide variety of articulated poses
  • Variable appearance/clothing
  • Complex backgrounds
  • Unconstrined illumination
  • Occlusions
  • Different Scales
feature sets
Feature Sets
  • Haar wavelets + SVM:
    • Papageorgiou & Poggio (2000)
    • Mohan et al (2001)
    • DePoortere et al (2002)
  • Rectangular differential features + adaBoost:
    • Viola & Jones(2001)
  • Parts based binary orientation position histogram + adaBoost:
    • Mikolajczk et al (2004)
  • Edge templates + nearest neighbor:
    • Gavrila & Philomen (1999)
  • Dynamic programming:
    • Felzenszwalb & Huttenlocher (2000),
    • Loffe & Forsyth (1999)
  • Orientation histograms:
    • C.F. Freeman et al (1996)
    • Lowe(1999)
  • Shape contexts:
    • Belongie et al (2002)
  • PCA-SIFT:
    • Ke and Sukthankar (2004)
slide9
Tested with
    • RGB
    • LAB
    • Grayscale
  • Gamma Normalization and Compression
    • Square root
    • Log
slide10

centered

diagonal

uncentered

cubic-corrected

Sobel

slide11
Histogram of gradient orientations

-Orientation -Position

    • Weighted by magnitude
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