Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS Presentation

Fast! 15 times faster than any previous approach 384 by 288 pixel images detected at 15 frames per second on a conventional 700 MHz Intel Pentium III

Robust Real-Time Face Detection 3 key contributors: - a new image representation: the “Integral Image” - a simple and effective classifier, based on the AdaBoost learning algorithm - combining the classifiers in a “cascade”

Detection basis: Features

Integral Image

Computing features

Classifier: using AdaBoost 160,000 features for every sub-window Very small number of these features can be combined to form an effective classifier AdaBoost: constrain each week classifier to depend on a single feature each stage of boosting = new week classifier selection = feature selection

First and Second Features Selected by AdaBoost

ROC curve for a 200 feature classifier

The Cascade combining successively more complex classifiers in a cascade structure 38 stages

ROC curves: cascaded vs. monolithic classifier -> not significantly different accuracy -> but the cascade class. almost 10 times faster

Results

Training dataset: 4916 images

ROC Curves for Face Detection

Comparing Viola-Jones with Other Systems

More: Detecting Walking Pedestrians Integrating image intensity with motion information Efficient, detects pedestrians at small scales, and has a very low false positive rate Works on low resolution images and under difficult weather conditions (rain, snow)

Extracting motion information

Training Set Samples

Questions?