Rapid object detection using a boosted cascade of simple features

Rapid object detection using a boosted cascade of simple features (2001)

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by Paul Viola , Michael Jones

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Datum	Value	Source
TITLE	Rapid object detection using a boosted cascade of simple features	INFERENCE
AUTHOR NAME	Paul Viola	SVM HeaderParse 0.2
AUTHOR AFFIL	Mitsubishi Electric Research Labs Compaq Cambridge Research Lab	SVM HeaderParse 0.2
AUTHOR ADDR	201 Broadway, 8th FL One Cambridge Center; Cambridge, MA 02139 Cambridge, MA 02142	SVM HeaderParse 0.2
AUTHOR NAME	Michael Jones	SVM HeaderParse 0.2
AUTHOR AFFIL	Mitsubishi Electric Research Labs Compaq Cambridge Research Lab	SVM HeaderParse 0.2
AUTHOR ADDR	201 Broadway, 8th FL One Cambridge Center; Cambridge, MA 02139 Cambridge, MA 02142	SVM HeaderParse 0.2
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[5]. 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. 1.	SVM HeaderParse 0.2
YEAR	2001	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	511--518	INFERENCE
VOLUME	1	INFERENCE
CITATIONS	17 found	ParsCit 1.0