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Online Visual Tracking (Hardcover, 1st ed. 2019)
Loot Price: R2,789
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Online Visual Tracking (Hardcover, 1st ed. 2019)
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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.
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