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Announcing the Final Examination of Mr. Omar Javed for the degree of Philosophy of Science
For object detection and categorization in the video stream,
a two step detection procedure is used. First, regions of interest are
determined using a novel hierarchical background subtraction algorithm
that uses color and gradient information for interest region detection (/projects/Knight/background.html).
Second, objects are located
and classified from within these regions using a weakly supervised
learning mechanism based on co-training that employs motion and appearance
features. The main contribution of this approach is that it is an online
procedure in which separate views (features) of the data are used for
co-training, while the combined view (all features) is used to make
classification decisions in a single boosted framework. The advantage of
this approach is that it requires only a few initial training samples and
can automatically adjust its parameters online to improve the detection
and classification performance. Once objects are detected and classified they are tracked in
individual cameras. Single camera tracking is performed using a voting
based approach that utilizes color and shape cues to establish
correspondence in individual cameras. The tracker has the capability to
handle multiple occluded objects. Next, the objects are tracked across a
forest of cameras with non-overlapping views. This is a hard problem
because of two reasons. First, the observations of an object are often
widely separated in time and space when viewed from non-overlapping
cameras. Secondly, the appearance of an object in one camera view might be
very different from its appearance in another camera view due to the
differences in illumination, pose and camera properties. To deal with the
first problem, the system learns the inter-camera relationships to
constrain track correspondences. These relationships are learned in the
form of multivariate probability density of space-time variables (object
entry and exit locations, velocities, and inter-camera transition times)
using Parzen windows. To handle the appearance change of an object as it
moves from one camera to another, we show that all color transfer
functions from a given camera to another camera lie in a low dimensional
subspace. The tracking algorithm learns this subspace by using
probabilistic principal component analysis and uses it for appearance
matching. The proposed system learns the camera topology and subspace of
inter-camera color transfer functions during a training phase. Once the
training is complete, correspondences are assigned using the maximum a
posteriori (MAP) estimation framework using both the location and
appearance cues (/projects/trackingNonOverlaping/NonoverlapTracking.html).
Dr.
Niels da Vitoria Lobo Dr. Mostafa Bassiouni Dr. David M. Nickerson The public is welcome to attend.
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