DocumentCode
629531
Title
Pedestrian detection with an improved Adaboost
Author
Tetik, Y.E. ; Bolat, B.
Author_Institution
Eng. Dept., Tech. Univ., Istanbul, Turkey
fYear
2013
fDate
19-21 June 2013
Firstpage
1
Lastpage
4
Abstract
This paper focuses on improving the performance of Adaboost (Adaptive Boosting) by using weak classifiers that make classification with a confidence score. Single thresholds and nearest neighbor classifiers are used as base classifiers. The proposed method is applied to the problem of pedestrian detection in still images. Haar-like basic features are used to construct weak classifiers.
Keywords
Haar transforms; image classification; learning (artificial intelligence); object detection; pedestrians; Adaboost; Haar-like basic feature; base classifier; confidence score; nearest neighbor classifier; pedestrian detection; still image; Bagging; Boosting; Classification algorithms; Computer vision; Feature extraction; Prediction algorithms; Training; Adaboost; Confidence Score; Haar like basic features; Nearest Neighbour; Weak Classifiers;
fLanguage
English
Publisher
ieee
Conference_Titel
Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on
Conference_Location
Albena
Print_ISBN
978-1-4799-0659-8
Type
conf
DOI
10.1109/INISTA.2013.6577626
Filename
6577626
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