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Enhancing Pedestrian Detection Using Histograms of Oriented Gradients

This study examines pedestrian detection challenges such as varied poses, clothing, backgrounds, lighting, occlusions, and scales. It explores different feature sets and classification methods to improve detection accuracy. The approach involves utilizing histograms of oriented gradients, along with wavelets, differential features, parts-based orientation histograms, edge templates, dynamic programming, orientation histograms, shape contexts, and PCA-SIFT while testing with various color spaces and normalization techniques.

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Enhancing Pedestrian Detection Using Histograms of Oriented Gradients

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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 • 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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