On-line Boosting and Vision
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. 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 (see also: tracking via on-line boosting)
- object detection (see also: conservative learning)
All approaches benefit significantly by the on-line training.
Principle
Downloads
- for some tracking videos please see Tracking via On-line Boosting .
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