On-line Learning-based Object Tracking Using Boosted Features

Abstract

The most informative and hard to classify examples are close to the decision boundary between object of interest and background. Gentle AdaBoost built on regression stumps focuses on hard examples that provide most new information during object tracking. They contribute to better learning of the classifier while tracking the object. The tracker is compared to recently proposed algorithm that uses on-line appearance models. The performance of the algorithm is demonstrated on freely available test sequences. The resulting algorithm runs in real-time.