Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.
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Subhash Challa is a Senior Principal Research Scientist at NICTA (National ICT Australia) VRL at the University of Melbourne (UoM). He is also one of the co-founders of SenSen Networks Pty Ltd and has been the Director and CTO of the company.
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Hardcover. Zustand: new. Hardcover. Kalman filter, particle filter, IMM, PDA, ITS, random sets. The number of useful object-tracking methods is exploding. But how are they related? How do they help track everything from aircraft, missiles and extra-terrestrial objects to people and lymphocyte cells? How can they be adapted to novel applications? Fundamentals of Object Tracking tells you how. Starting with the generic object-tracking problem, it outlines the generic Bayesian solution. It then shows systematically how to formulate the major tracking problems maneuvering, multiobject, clutter, out-of-sequence sensors within this Bayesian framework and how to derive the standard tracking solutions. This structured approach makes very complex object-tracking algorithms accessible to the growing number of users working on real-world tracking problems and supports them in designing their own tracking filters under their unique application constraints. The book concludes with a chapter on issues critical to successful implementation of tracking algorithms, such as track initialization and merging. In recent years there have been major advances in the use of object tracking technology for both military and civilian applications. This book provides a solid foundation to this field and its methods for the growing number of students, engineers and researchers working on real-world tracking problems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9780521876285
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Zustand: New. Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples. Num Pages: 392 pages, 60 b/w illus. 1 colour illus. BIC Classification: TJFM. Category: (U) Tertiary Education (US: College). Dimension: 253 x 183 x 27. Weight in Grams: 918. . 2011. Hardcover. . . . . Bestandsnummer des Verkäufers V9780521876285
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