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

Alhayat Ali Mekonnen (1) , Frédéric Lerasle (1) , Ariane Herbulot (1)

1 LAAS-RAP - Équipe Robotique, Action et Perception

In 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.

Domaines: Traitement du signal et de l'image [eess.SP]

Alhayat Ali Mekonnen, Frédéric Lerasle, Ariane Herbulot. Pareto-Front Analysis and AdaBoost for Person Detection Using Heterogeneous Features. IEEE International Conference on Systems, Man and Cybernetics (SMC 2013), Oct 2013, Manchester, France. pp.4316-4321, ⟨10.1109/SMC.2013.736⟩. ⟨hal-02024560⟩