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
AdaBoost
Boosting (machine learning)
Pedestrian detection
Artificial intelligence
Computer science
Pattern recognition (psychology)
Random subspace method
Machine learning
Pedestrian
Haar-like features
Support vector machine
Object detection
Face detection
Engineering
Facial recognition system
Subject Areas
Video Surveillance and Tracking Methods
·Computer Vision and Pattern Recognition ·Physical Sciences
Face and Expression Recognition
·Computer Vision and Pattern Recognition ·Physical Sciences
Anomaly Detection Techniques and Applications
·Artificial Intelligence ·Physical Sciences
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