@inbook{798a8617fb4747b88f6f65182c436e72,
title = "Reconstruction-Free Compressive Vision for Surveillance Applications",
abstract = "Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.",
keywords = "compressed sensing, deep learning, sparse representations, surveillance, track-before-detect",
author = "Henry Braun and Pavan Turaga and Andreas Spanias and Sameeksha Katoch and Suren Jayasuriya and Cihan Tepedelenlioglu",
note = "Funding Information: We thank the Fermilab staff and the technical staffs of the participating institutions for their vital contributions. This work was supported by the U.S. Department of Energy and National Science Foundation; the Italian Istituto Nazionale di Fisica Nucleare; the Ministry of Education, Science and Culture of Japan; the National Sciences and Engineering Research Council of Canada; the National Science Council of the Republic of China; the A. P. Sloan Foundation; and the Swiss National Science Foundation. Publisher Copyright: Copyright {\textcopyright} 2019 by Morgan & Claypool.",
year = "2019",
doi = "10.2200/S00914ED2V01Y201904SPR017",
language = "English (US)",
series = "Synthesis Lectures on Signal Processing",
publisher = "Morgan and Claypool Publishers",
number = "1",
pages = "1--100",
booktitle = "Synthesis Lectures on Signal Processing",
edition = "1",
}