Robust Real Time Object Detection
Paul Viola and Mike Jones
MERL/MIT/Campaq CRL
This paper describes a visual object detection framework that is
capable of processing images extremely rapidly while achieving
high detection rates. There are three key contributions. The
first is the introduction of a new image representation called
the "ntegral 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 and yields extremely efficient
classifiers. The third contribution is a method for combining
classifiers in a "cascade" which allows background regions of
the image to be quickly discarded while spending more
computation on promising object like regions. A set of
experiments in the domain of face detection are presented. The
system yields face detection performance comparable to the best
previous systems. Implemented on a conventional desktop, face
detection proceeds at 15 frames per second.