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Embargoed until 2009-06-16
Copyright: Wang, Jue
Embargoed until 2009-06-16
Copyright: Wang, Jue
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Abstract
Object tracking is an active research topic in computer vision and has appli-
cation in several areas, such as event detection and robotics. Vehicle tracking
is used in Intelligent Transport System (ITS) and surveillance systems. Its re-
liability is critical to the overall performance of these systems. Feature-based
methods that are used to represent distinctive content in visual frames are one
approach to vehicle tracking. Existing feature-based tracking systems can only
track vehicles under ideal conditions. They have difficulties when used under
a variety of conditions, for example, during both the day and night. They
are highly dependent on stable local features that can be tracked for a long
time period. These local features are easily lost because of their local property
and image noise caused by factors such as, headlight reflections and sun glare.
This thesis presents a new approach, addressing the reliability issues mentioned
above, tracking whole feature groups composed of feature points extracted with
the Scale Invariant Feature Transform (SIFT) algorithm. A feature group in-
cludes several features that share a similar property over a time period and can
be tracked to the next frame by tracking individual feature points inside it. It
is lost only when all of the features in it are lost in the next frame. We cre-
ate these feature groups by clustering individual feature points using distance,
velocity and acceleration information between two consecutive frames. These
feature groups are then hierarchically clustered by their inter-group distance,
velocity and acceleration information. Experimental results show that the pro-
posed vehicle tracking system can track vehicles with the average accuracy of
over 95%, even when the vehicles have complex motions in noisy scenes. It gen-
erally works well even in difficult environments, such as for rainy days, windy
days, and at night. We are surprised to find that our tracking system locates
and tracks motor bikes and pedestrians. This could open up wider opportunities
and further investigation and experiments are required to confirm the tracking
performance for these objects. Further work is also required to track more com-
plex motions, such as rotation and articulated objects with different motions on
different parts.