MPhilThe aim of the thesis is to create and validate models of visual attention. To
this extent, a novel unsupervised object detection and tracking framework has been
developed by the author. It is demonstrated on people, faces and moving objects
and the output is integrated in modelling of visual attention. The proposed approach
integrates several types of modules in initialisation, target estimation and validation.
Tracking is rst used to introduce high-level features, by extending a popular model
based on low-level features[1]. Two automatic models of visual attention are further
implemented. One based on winner take it all and inhibition of return as the mech-
anisms of selection on a saliency model with high- and low-level features combined.
Another which is based only on high-level object tracking results and statistic proper-
ties from the collected eye-traces, with the possibility of activating inhibition of return
as an additional mechanism. The parameters of the tracking framework thoroughly
investigated and its success demonstrated. Eye-tracking experiments show that high-
level features are much better at explaining the allocation of attention by the subjects
in the study. Low-level features alone do correlate signi cantly with real allocation
of attention. However, in fact it lowers the correlation score when combined with
high-level features in comparison to using high-level features alone. Further, ndings
in collected eye-traces are studied with qualitative method, mainly to discover direc-
tions in future research in the area. Similarities and dissimilarities between automatic
models of attention and collected eye-traces are discusse
