"Video Tracking" provides a comprehensive treatment of the
fundamental aspects of algorithm and application development for
the task of estimating, over time, the position of objects of
interest seen through cameras. Starting from the general problem
definition and a review of existing and emerging video tracking
applications, the book discusses popular methods, such as those
based on correlation and gradient-descent. Using practical
examples, the reader is introduced to the advantages and
limitations of deterministic approaches, and is then guided toward
more advanced video tracking solutions, such as those based on the
Bayes' recursive framework and on Random Finite Sets.
Key features: Discusses the design choices and implementation
issues required to turn the underlying mathematical models into a
real-world effective tracking systems. Provides block diagrams and
simil-code implementation of the algorithms. Reviews methods to
evaluate the performance of video trackers - this is identified as
a major problem by end-users.
The book aims to help researchers and practitioners develop
techniques and solutions based on the potential of video tracking
applications. The design methodologies discussed throughout the
book provide guidelines for developers in the industry working on
vision-based applications. The book may also serve as a reference
for engineering and computer science graduate students involved in
vision, robotics, human-computer interaction, smart environments
and virtual reality programmes
General
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