Ruxandra Domenig, An Overview and Classifcation of Mediated Query Systems
Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS 148 - Presentation 04.28.2005.
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Transcript of Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS 148 - Presentation 04.28.2005.
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”
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
ROC curves: cascaded vs. monolithic classifier
-> not significantly different accuracy
-> but the cascade class. almost 10 times faster
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)