Title :
On-line Boosting and Vision
Author :
Grabner, Helmut ; Bischof, Horst
Author_Institution :
Graz University of Technology
Abstract :
Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.
Keywords :
Application software; Boosting; Computer graphics; Computer vision; Feature extraction; Machine learning; Machine learning algorithms; Object detection; Support vector machines; Training data;
Conference_Titel :
Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
Print_ISBN :
0-7695-2597-0
DOI :
10.1109/CVPR.2006.215