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Issue No.02 - February (2007 vol.29)
pp: 261-271
ABSTRACT
We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pixels in the next frame as either belonging to the object or the background, giving a confidence map. The peak of the map and, hence, the new position of the object, is found using mean shift. Temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained online during tracking. We show a realization of this method and demonstrate it on several video sequences.
INDEX TERMS
AdaBoost, visual tracking, video analysis, concept learning.
CITATION
Shai Avidan, "Ensemble Tracking", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.29, no. 2, pp. 261-271, February 2007, doi:10.1109/TPAMI.2007.35
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