Histograms of Oriented Gradients for Human Detection

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Histograms of Oriented Gradients for Human Detection (2005)

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by Navneet Dalal , Bill Triggs

Venue:	In CVPR
Citations:	3735 - 9 self

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@INPROCEEDINGS{Dalal05histogramsof,
    author = {Navneet Dalal and Bill Triggs},
    title = {Histograms of Oriented Gradients for Human Detection},
    booktitle = {In CVPR},
    year = {2005},
    pages = {886--893}
}

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Abstract

We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor 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. 1

Keyphrases

human detection oriented gradient feature set challenging dataset containing pose variation fine orientation binning descriptor block large range high-quality local contrast normalization annotated human image robust visual object recognition fine-scale gradient near-perfect separation linear svm new approach coarse spatial binning original mit pedestrian database good result test case

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