[PDF] Deep Scene Understanding Using Rf And Its Fusion With Other Modalities eBook

Author : Akash Deep Singh
Publisher :
Page : 0 pages
File Size : 38,61 MB
Release : 2023
Category :
ISBN :

GET BOOK

Rich scene understanding is a critical first step in creating autonomous systems with situational awareness -- i.e. systems that can not only perceive and comprehend their environments but also project what the future states are going to be. Current vision-based methods of tackling this problem are inadequate as cameras are restricted to the visible spectrum. While they can detect objects, track movements, and make inferences about human expressions, they suffer from several challenges such as lack of depth information and weakness to bad weather conditions. Moreover, there are many other modalities in which information is present around us, and relying solely on one makes it susceptible to a higher chance of failure. Through my thesis, I aim to include RF (radio-frequency) modality in scene understanding since RF has both complementary and supplementary properties to vision. My hypothesis is that by fusing RF with vision, one can create a richer understanding of their scene which I call 'deep scene understanding'. There are four key enablers to deep scene understanding -- (1) Detection of objects' states and activities, (2) Localization of objects in a scene and tracking them, (3) Developing methods to train machine learning models over RF data, and (4) Understanding privacy and societal impacts of instrumenting spaces with sensors. RF comes with its own set of challenges that make this sort of integration hard. Additionally, instrumenting spaces with sensors such as RF sensors itself can lead to privacy concerns. In solving these challenges, we present -- (1) a framework to detect human activities using a mmWave radar that can ingest sparse and noisy radar point clouds and output what activity is being performed in the scene. (2) a framework to detect, identify and localize hidden objects such as cameras in a scene that may be monitoring a user but are not visible to the naked eye. (3) a radar-camera fusion framework that can estimate dense depth in a scene from a sparse radar point cloud and an image. (4) A self-supervised learning approach that can leverage mutual information between a camera and a radar to train the radar. (5) A user study to understand the privacy perceptions of users when spaces are equipped with sensors.

Author : Michael Yang
Publisher : Academic Press
Page : 422 pages
File Size : 10,18 MB
Release : 2019-07-16
Category : Computers
ISBN : 0128173599

GET BOOK

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Author : Gang Xiao
Publisher : Springer Nature
Page : 415 pages
File Size : 27,69 MB
Release : 2020-08-31
Category : Computers
ISBN : 9811548676

GET BOOK

This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories – pixel, feature and decision – presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.

Author : Li Deng
Publisher :
Page : 212 pages
File Size : 36,29 MB
Release : 2014
Category : Machine learning
ISBN : 9781601988140

GET BOOK

Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks

Author : Zhiyuan Liu
Publisher : Springer Nature
Page : 319 pages
File Size : 22,85 MB
Release : 2020-07-03
Category : Computers
ISBN : 9811555737

GET BOOK

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Author : Thomas B. Moeslund
Publisher : Springer Science & Business Media
Page : 633 pages
File Size : 29,57 MB
Release : 2011-10-08
Category : Computers
ISBN : 0857299972

GET BOOK

This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.

Author : Gustau Camps-Valls
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 10,57 MB
Release : 2021-08-18
Category : Technology & Engineering
ISBN : 1119646162

GET BOOK

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Author : Mettu Srinivas
Publisher : John Wiley & Sons
Page : 372 pages
File Size : 24,79 MB
Release : 2021-08-10
Category : Computers
ISBN : 1119769248

GET BOOK

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.