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

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

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  1. Pedestrian DetectionHistograms of Oriented Gradients for Human DetectionNavneet Dalal and Bill Triggs CVPR ‘05 Pete Barnum March 8, 2006

  2. Challenges • Wide variety of articulated poses • Variable appearance/clothing • Complex backgrounds • Unconstrined illumination • Occlusions • Different Scales

  3. Slides from Sminchisescu

  4. Slides from Sminchisescu

  5. Slides from Sminchisescu

  6. 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)

  7. Tested with • RGB • LAB • Grayscale • Gamma Normalization and Compression • Square root • Log

  8. centered diagonal uncentered cubic-corrected Sobel

  9. Histogram of gradient orientations -Orientation -Position • Weighted by magnitude

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