Diploma and Master Theses (authored and supervised):

F. Seitner:
"Robust detection and tracking of objects";
Supervisor: A. Hanbury; Institut für rechnergestützte Automation, 2006.

English abstract:
Due to the increasing availability of fast and cheap hardware in the past few years, today a wide range of
complex visual tracking tasks is possible. E cient mathematical methods can provide a high robustness
which also makes visual tracking interesting for many industrial purposes. However, the high demands on
quality and speed still provide a major challenge for each tracking application. In this thesis a tracking
system is introduced, which tries to address both demands appropriately by using currently available
algorithms to quickly track pedestrians in video streams. By combining these well-proved algorithms, a
good solution regarding computational complexity, accuracy and stability is obtained.
To achieve this task, a fast object detector similar to the approach of Viola et al. [VJS03] is used as
one component in this tracking system. This detector uses Haar-like features which are very fast to
compute and makes a quick pedestrian detection in a frame possible. Next to the detection system,
an adaptive background model sub-divides each frame into foreground and background regions. As a
compromise between complexity and robustness a single-mode parametric background model based on
normal distributions and wrapped normal distributions is used. Both background model and detector
are combined to provide the tracking system with locations of pedestrian-like regions and to sub-divide
the body into three parts: head, upper body and lower body. After this segmentation into ner tracking
units a set of colour and spatial features for further tracking is extracted from each part individually.
Individual and spatially separated body parts also provide the possibility to use colour histograms in
a spatial sense. Moreover, an appearance model provides accurate solutions and approximations when
occlusions or missing detections occur.

tracking, detection, object recognition, color, hue, circular statistics

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.