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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:809 - 3 self
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BibTeX

@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

Citations

3104 Distinctive image features from scale invariant keypoints - Lowe
1086 Making large-scale SVM learning practical - Joachims - 1999
775 A performance evaluation of local descriptors,‖ in - Mikolajczyk, Schmid - 2004
670 Scale and affine invariant interest point detectors - Mikolajczyk, Schmid - 2004
456 The visual analysis of human movement: A survey - Gavrila - 1999
246 Detecting pedestrians using patterns of motion and appearance - Viola, Jones, et al. - 2003
237 PCA-SIFT: A more distinctive representation for local image descriptors - Ke, Sukthankar - 2004
186 Example-based object detection in images by components - Mohan, Papageorgiou, et al. - 2001
159 A trainable system for object detection - Papageorgiou, Poggio
149 Real-time object detection for “smart“ vehicles - Gavrila, Philomin - 1999
116 M.: Orientation histograms for hand gesture recognition - Freeman, Roth - 1995
114 Efficient matching of pictorial structures - Felzenszwalb, Huttenlocher - 2000
114 Human Detection Based on a Probabilistic Assembly - Mikolajczyk, Schmid, et al.
114 Spatial mapping in the primate sensory projection: analytic structure and relevance to perception - SCHWARTZ - 1977
88 Object Detection Using the Statistics of Parts - Schneiderman, Kanade - 2004
77 Probabilistic Methods for Finding People - Ioffe, Forsyth - 2001
51 Computer vision for computer games - Freeman, Tanaka, et al. - 1996
22 Vison-based pedestrian detection: The protector system - Gravila, Giebel, et al. - 2004
4 Method of and apparatus for pattern recognition - McConnell - 1986
3 Efficient pedestrian detection: a test case for svm based categorization - Deporrtere, Cant, et al. - 2002
1 Matching shapes. The 8th ICCV - Belongie, Malik, et al. - 2001
1 Learning to parse pictures of people. The 7th ECCV - Ronfard, Schmid, et al. - 2002
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