Robust Real Time Object Detection

Paul Viola and Mike Jones


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.

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