Histograms of Oriented Gradients for Human Detection

Histograms of Oriented Gradients for Human Detection,10.1109/CVPR.2005.177,Navneet Dalal,Bill Triggs

Histograms of Oriented Gradients for Human Detection   (Citations: 1704)
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We study the question of feature sets for robust visual ob- ject recognition, adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors sig- nicantly outperform existing feature sets for human detec- tion. We study the inuence of each stage of the computation on performance, concluding that ne-scale gradients, ne orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping de- scriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
Conference: Computer Vision and Pattern Recognition - CVPR , vol. 1, pp. 886-893, 2005
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