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Advanced Pedestrian Detection Using Histograms of Oriented Gradients

Explore the challenges and solutions in human detection with Histograms of Oriented Gradients (HOG) by Dalal and Triggs. Learn about the feature sets, testing with varied color spaces, and gradient orientation histograms. Enhance your understanding of pedestrian detection techniques.

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Advanced Pedestrian Detection Using Histograms of Oriented Gradients

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

  2. Challenges • Wide variety of articulated poses • Variable appearance/clothing • Complex backgrounds • Unconstrained 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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