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Visual Object Tracking using Deep Learning
Loot Price: R2,738
Discovery Miles 27 380
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Visual Object Tracking using Deep Learning
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Total price: R2,758
Discovery Miles: 27 580
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The text comprehensively discusses tracking architecture under
stochastic and deterministic frameworks and presents experimental
results under each framework with qualitative and quantitative
analysis. It covers deep learning techniques for feature
extraction, template matching, and training the networks in
tracking algorithms. Discusses performance metrics for visual
tracking in comparing the efficiency and effectiveness of available
datasets. Covers performance metrics such as center location error,
F-Measure, area under control, distance precision, and overlap
precision. Compares the performance of deep learning trackers with
traditional methods, wherein hand-crafted features were fused to
reduce the computational complexity. Illustrates stochastic
framework for visual tracking such as probabilistic methods in the
Bayesian framework for state estimation. The text presents both
traditional and advanced methods such as stochastic, deterministic,
generative, discriminative framework, and deep learning-based
appearance models. It further highlights the use of deep learning
for feature extraction, template matching, and training the
networks in tracking algorithms. The book covers context-aware, and
super pixel-based correlation filter tracking. The text is
primarily written for senior undergraduate, graduate students, and
academic researchers in the fields of electrical engineering,
electronics and communication engineering, computer engineering,
and information technology.
General
Imprint: |
Taylor & Francis
|
Country of origin: |
United Kingdom |
Release date: |
November 2023 |
First published: |
2023 |
Authors: |
Ashish Kumar
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Dimensions: |
234 x 156mm (L x W) |
Pages: |
272 |
ISBN-13: |
978-1-03-249053-3 |
Categories: |
Books
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LSN: |
1-03-249053-5 |
Barcode: |
9781032490533 |
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