Human Activity Recognition and Prediction
Human Activity Recognition and Prediction

Author: Yun Fu

Publisher: Springer

Published: 2015-12-23

Total Pages: 179

ISBN-13: 3319270044

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This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.

Deep Learning for Time Series Forecasting
Deep Learning for Time Series Forecasting

Author: Jason Brownlee

Publisher: Machine Learning Mastery

Published: 2018-08-30

Total Pages: 572

ISBN-13:

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Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

Computer Vision - ECCV 2008
Computer Vision - ECCV 2008

Author: David Hutchison

Publisher:

Published: 2008

Total Pages: 0

ISBN-13: 9788354088684

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The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed proceedings of the 10th European Conference on Computer Vision, ECCV 2008, held in Marseille, France, in October 2008. The 243 revised papers presented were carefully reviewed and selected from a total of 871 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, stereo, people and face recognition, object tracking, matching, learning and features, MRFs, segmentation, computational photography and active reconstruction.

Deep Learning for Human Activity Recognition
Deep Learning for Human Activity Recognition

Author: Xiaoli Li

Publisher: Springer Nature

Published: 2021-02-17

Total Pages: 139

ISBN-13: 9811605750

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This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.

Pervasive Computing
Pervasive Computing

Author: Alois Ferscha

Publisher: Springer Science & Business Media

Published: 2004-04-07

Total Pages: 375

ISBN-13: 3540218351

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nd Welcome to the proceedings of PERVASIVE 2004, the 2 International C- ference on Pervasive Computing and the premier forum for the presentation and appraisal of the most recent and most advanced research results in all - undational and applied areas of pervasive and ubiquitous computing. Consi- ring the half-life period of technologies and knowledge this community is facing, PERVASIVE is one of the most vibrant, dynamic, and evolutionary among the computer-science-related symposia and conferences. The research challenges, e?orts, and contributions in pervasive computing have experienced a breathtaking acceleration over the past couple of years, mostly due to technological progress, growth, and a shift of paradigms in c- puter science in general. As for technological advances, a vast manifold of tiny, embedded, and autonomous computing and communication systems have st- ted to create and populate a pervasive and ubiquitous computing landscape, characterized by paradigms like autonomy, context-awareness, spontaneous - teraction, seamless integration, self-organization, ad hoc networking, invisible services, smart artifacts, and everywhere interfaces. The maturing of wireless networking, miniaturized information-processing possibilities induced by novel microprocessor technologies, low-power storage systems, smart materials, and technologies for motors, controllers, sensors, and actuators envision a future computing scenario in which almost every object in our everyday environment will be equipped with embedded processors, wireless communication facilities, and embedded software to perceive, perform, and control a multitude of tasks and functions.

Deep Learning through Sparse and Low-Rank Modeling
Deep Learning through Sparse and Low-Rank Modeling

Author: Zhangyang Wang

Publisher: Academic Press

Published: 2019-04-12

Total Pages: 296

ISBN-13: 0128136596

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Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.

Big Data Analytics for Sensor-Network Collected Intelligence
Big Data Analytics for Sensor-Network Collected Intelligence

Author: Hui-Huang Hsu

Publisher: Morgan Kaufmann

Published: 2017-02-02

Total Pages: 328

ISBN-13: 012809625X

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Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS - Contains contributions from noted scholars in computer science and electrical engineering from around the globe - Provides a broad overview of recent developments in sensor collected intelligence - Edited by a team comprised of leading thinkers in big data analytics

IoT Sensor-Based Activity Recognition
IoT Sensor-Based Activity Recognition

Author: Md Atiqur Rahman Ahad

Publisher: Springer Nature

Published: 2020-07-30

Total Pages: 214

ISBN-13: 3030513793

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This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily human behavior, and fitness monitoring. In addition, the complexities, challenges, and design issues involved in data collection, processing, and other fundamental stages along with datasets, methods, etc., are discussed in detail. The book offers a valuable resource for readers in the fields of pattern recognition, human–computer interaction, and the Internet of Things.

Human Activity Recognition Challenge
Human Activity Recognition Challenge

Author: Md Atiqur Rahman Ahad

Publisher: Springer Nature

Published: 2020-11-20

Total Pages: 126

ISBN-13: 9811582696

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The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).