PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

130 Chapter 9. Visual Object Tracking
generated by applying several affine transformations on the marked target patch of the first
frame. As can be seen, the value of α has significant influence on the performance of OS-
ERB. However, it can also be observed that on all sequences for a wide range of α OSERB
is able to outperform the competing methods. In particular, OSERB outperforms the competing
methods on scenarios where both the target and the background are changing, e.g.,
David, and usually a more adaptive classifier would be expected to perform better. It also
outperforms the other methods on sequences where the target object becomes occluded,
e.g., Face occluded 2, and usually a less adaptive classifier would be preferred. Another
surprising result of this experiment is the performance of the non-adaptive tracker which
is able to match the performance of OAB on three out of four scenarios and significantly
outperforms OAB on the Girl sequence.