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On-line Learning-based Object Tracking Using Boosted Features

Kwolek, Bogdan

The 5th International Conference on Computer Vision Systems, 2007
Bielefeld, 21. - 24. März 2007

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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.


Keywords: visual tracking, on-line learning, color image processing, human machine interaction
Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Kwolek, Bogdan  (2007)  On-line Learning-based Object Tracking Using Boosted Features. The 5th International Conference on Computer Vision Systems, 2007


URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/55



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