This book presents the state of the art in online visual tracking,
including the motivations, practical algorithms, and experimental
evaluations. Visual tracking remains a highly active area of research in
Computer Vision and the performance under complex scenarios has
substantially improved, driven by the high demand in connection with
real-world applications and the recent advances in machine learning. A
large variety of new algorithms have been proposed in the literature
over the last two decades, with mixed success.
Chapters 1 to 6 introduce readers to tracking methods based on online
learning algorithms, including sparse representation, dictionary
learning, hashing codes, local model, and model fusion. In Chapter 7,
visual tracking is formulated as a foreground/background segmentation
problem, and tracking methods based on superpixels and end-to-end deep
networks are presented. In turn, Chapters 8 and 9 introduce the
cutting-edge tracking methods based on correlation filter and deep
learning. Chapter 10 summarizes the book and points out potential future
research directions for visual tracking.
The book is self-contained and suited for all researchers, professionals
and postgraduate students working in the fields of computer vision,
pattern recognition, and machine learning. It will help these readers
grasp the insights provided by cutting-edge research, and benefit from
the practical techniques available for designing effective visual
tracking algorithms. Further, the source codes or results of most
algorithms in the book are provided at an accompanying website.
