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This book shows how machine learning can detect moving objects in a
digital video stream. The authors present different background
subtraction approaches, foreground segmentation, and object
tracking approaches to accomplish this. They also propose an
algorithm that considers a multimodal background subtraction
approach that can handle a dynamic background and different
constraints. The authors show how the proposed algorithm is able to
detect and track 2D & 3D objects in monocular sequences for
both indoor and outdoor surveillance environments and at the same
time, also able to work satisfactorily in a dynamic background and
with challenging constraints. In addition, the shows how the
proposed algorithm makes use of parameter optimization and adaptive
threshold techniques as intrinsic improvements of the Gaussian
Mixture Model. The presented system in the book is also able to
handle partial occlusion during object detection and tracking. All
the presented work and evaluations were carried out in offline
processing with the computation done by a single laptop computer
with MATLAB serving as software environment.
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