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

Author

Mekonnen, A.A. ; Lerasle, Frederic ; Herbulot, A.

Author_Institution

LAAS, France Univ. de Toulouse, Toulouse, France

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

4316

Lastpage

4321

Abstract

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.

Keywords

Pareto analysis; feature extraction; learning (artificial intelligence); object detection; AdaBoost; INRIA public dataset; Pareto-front analysis; boosted cascade person detection framework; cascade node; dominant feature selection; feature selection scheme; heterogeneous features; Boosting; Decision trees; Detectors; Feature extraction; Histograms; Support vector machines; Training; Ad-aBoost; Feature Selection; Pareto-Front Analysis; Pattern Recognition; Person Detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.736

Filename

6722489

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=678117