Very recently tracking was approached using classification techniques such

as support vector machines. The object to be tracked is discriminated by a

classifier from the background. In a similar spirit we propose a novel on-line

AdaBoost feature selection algorithm for tracking. The distinct advantage of

our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object

(e.g. out of plane rotations, illumination changes) are handled quite naturally.

Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using

fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences.