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Rapid Object Detection Using a Boosted Cascade of Simple Features (2001)

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by Paul Viola , Michael Jones
Citations:1371 - 6 self
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BibTeX

@INPROCEEDINGS{Viola01rapidobject,
    author = {Paul Viola and Michael Jones},
    title = {Rapid Object Detection Using a Boosted Cascade of Simple Features},
    booktitle = {},
    year = {2001},
    pages = {511--518}
}

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Abstract

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

Citations

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764 Neural network-based face detection - Rowley, Baluja, et al. - 1996
688 The Design and Use of Steerable Filters - Freeman, Adelson - 1991
688 A model of saliency-based visual attention for rapid scene analysis - Itti, Koch, et al. - 1998
606 W: Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. The Annals of Statistics - Schapire, Freund, et al. - 1998
454 Training support vector machines: an application to face detection - Osuna, Freund, et al. - 1997
300 A statistical method for 3d object detection applied to faces and cars - Schneiderman, Kanade - 2000
217 P.: Boosting image retrieval - Tieu, Viola - 2000
216 A General Framework for Object Detection - Papageorgiou, Oren, et al. - 1998
210 Statistical Pattern Recognition - Webb - 1999
200 Modeling visual-attention via selective tuning - Tsotsos, Culhane, et al. - 1995
182 Summed-Area Tables for Texture Mapping - Crow - 1984
98 A SNoW-Based Face Detector - Yang, Roth, et al. - 2000
69 Coarse-to-Fine Face Detection - Fleuret, Geman - 2001
66 Joint Induction of Shape Features and Tree Classifiers - Amit, Geman, et al. - 1997
48 Overcomplete steerable pyramid filters and rotation invariance - Greenspan, Belongie, et al. - 1994
27 Example-based learning for viewbased face detection - Sung, Poggio - 1998
1 Overcomplete steerable pyramid filISBN 0-7695-1272-0/01 $10.00 (C) 2001 IEEE ters and rotation invariance - Greenspan, Belongie, et al. - 1994
The National Science Foundation
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