Pareto-Front Analysis and AdaBoost for Person Detection Using Heterogeneous Features

International audienceIn this paper, a boosted cascade person detection framework with heterogeneous pool of features is presented. The framework unveils a new feature selection scheme based on Pareto-Front analysis and AdaBoost. At each cascade node, Pareto-Front analysis is used to select dominant features thereby reducing the total number of features to a size easily manageable by AdaBoost. The final detector achieves a very low Miss Rate of 0.07 at 10-4 False Positives Per Window on the INRIA public dataset