PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

9.3. Experiments 129
9.3.4 Benchmark Sequences
In this part of the experimental section, we will quantitatively evaluate and compare all
of the discussed on-line learning variants in this thesis. Therefore, we use eight publicly
available sequences including variations in illumination, pose, scale, rotation and appearance,
and partial occlusions. The sequences Sylvester and David are taken from [Ross
et al., 2008] and Face Occlusion 1 is taken from [Adam et al., 2006], respectively. Face
occlusion 2, coke, Girl, Tiger1 and Tiger2 are taken from [Babenko et al., 2009b]. All
video frames are gray scale and of size 320 × 240.
Evaluation Method
To show the real accuracy of the compared tracking methods, we use the overlap-criterion
of the VOC Challenge [Everingham et al., 2007], which is defined as
R T ∩ R GT /R T ∪ R GT , (9.8)
where R T is the tracking rectangle and R GT the groundtruth. Since we are interested in the
alignment accuracy of our tracker and the tracked object, rather than just computing the
raw distance we measure the accuracy of a tracker by computing the average detection
score for the entire video. Since it is very difficult or nearly impossible to reach the
maximum of 1.0 for this criterion, a value larger than 0.8 can be seen as nearly perfect
tracking result, 0.5 to 0.8 would be acceptable.
Tracking performance
As it is the main purpose to compare the learning methods, we use the same simple
Haar-like features as representation for all the learning methods, did not implement any
rotation or scale search and avoid any other engineering methods. All trackers where
initialized by generating 100 virtual samples [Girosi and Chan, 1995] at the first frame
using affine transformations. For all boosting methods, we used 50 selectors with each 50
weak classifiers. For all variants of random forests we used 50 trees and a feature pool of
randomly selected 500 Haar features.
Influence of the convex combination In the first experiment, we depict the performance
of OSERB on four tracking sequences depending on different settings for α. We
run each tracker 5 times and report the median with respect to the average overlap score
on the whole sequence. The results are given in Figure 9.13 together with an on-line
AdaBoost (OAB) performing self-learning and a static classifier only trained at the first
frame (PRIOR). All classifiers where trained using additional “virtual” positive samples