Combining Adaboost learning and evolutionary search to select features for real-time object detection

Recently, P. Viola and M.J. Jones (2001) presented a method for real-time object detection in images using a boosted cascade of simple features. In This work we show how an evolutionary algorithm can be used within the Adaboost framework to find new features providing better classifiers. The evolutionary algorithm replaces the exhaustive search over all features so that even very large feature sets can be searched in reasonable time. Experiments on two different sets of images prove that by the use of evolutionary search we are able to find object detectors that are faster and have higher detection rates.