Deep Learning Methods For Video Based Human Activity Recognition In Industrial Settings
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Deep Learning Methods For Video Based Human Activity Recognition In Industrial Settings
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Author : Behnoosh Parsa
language : en
Publisher:
Release Date : 2020
Deep Learning Methods For Video Based Human Activity Recognition In Industrial Settings written by Behnoosh Parsa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
With increasingly high interest in assistive robots and smart surveillance systems, we need a powerful perception mechanism to be able to describe the events in a scene. However, achieving accurate perception models is not trivial, since, even for one perception task there are unlimited possible scenarios. Hoping to develop analytically driven models seems too optimistic for such systems; hence, Supervised Learning as a sub-field of function approximation has become very popular in robotic perception. Supervised learning is the task of learning a function that maps an input to an output based on example input-output pairs. Scene understanding is even more involved when it comes to solving Human Action Recognition (HAR) problems. In HAR the task is to classify human activities from an image or determine atomic actions composing the activity in a video. In video-based HAR, there are exponentially many ways that humans can perform the same task. Besides, the variety in posture and speed at which people perform activities makes solving HAR tasks even more challenging. Therefore, models should be designed to learn common underlying spatial and temporal properties of human activity to achieve generalizability. This thesis is dedicated to designing perception models for recognizing human actions and determining the ergonomic risk associated with them. Specifically, Part I focus on solving the Human Activity Segmentation (HAS) problem in long videos, which is the task of semantically segmenting long videos into distinct actions in an offline framework. In Part II, we present our designs for solving online-HAR problems to recognize human activities in the observed batch of frames. Since, the performance of computer vision algorithms also depends on the quality and relevance of the training data, in Part I, we introduce a new dataset for an indoor object manipulation task called the University of Washington Indoor Object Manipulation (UW-IOM).
Human Activity Recognition And Prediction
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Author : Yun Fu
language : en
Publisher: Springer
Release Date : 2015-12-23
Human Activity Recognition And Prediction written by Yun Fu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-23 with Technology & Engineering categories.
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.
Vision Based Human Activity Recognition
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Author : Zhongxu Hu
language : en
Publisher: Springer Nature
Release Date : 2022-04-22
Vision Based Human Activity Recognition written by Zhongxu Hu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-22 with Computers categories.
This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human–machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.
Human Activity Recognition Challenge
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Author : Md Atiqur Rahman Ahad
language : en
Publisher: Springer Nature
Release Date : 2020-11-20
Human Activity Recognition Challenge written by Md Atiqur Rahman Ahad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-20 with Technology & Engineering categories.
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).
Deep Learning For Human Activity Recognition
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Author : Xiaoli Li
language : en
Publisher: Springer Nature
Release Date : 2021-02-17
Deep Learning For Human Activity Recognition written by Xiaoli Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-17 with Computers categories.
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.
Sensor And Video Based Activity And Behavior Computing
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Author : Md Atiqur Rahman Ahad
language : en
Publisher: Springer Nature
Release Date : 2022-05-03
Sensor And Video Based Activity And Behavior Computing written by Md Atiqur Rahman Ahad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-03 with Technology & Engineering categories.
This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20–22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition—both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.
Recognition Of Humans And Their Activities Using Video
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Author : Rama Chellappa
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2006-01-01
Recognition Of Humans And Their Activities Using Video written by Rama Chellappa and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-01 with Technology & Engineering categories.
The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusion of different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.
Wifi Based Activity Recognition With Deep Learning
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Author : Fangxin Wang
language : en
Publisher:
Release Date : 2020
Wifi Based Activity Recognition With Deep Learning written by Fangxin Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Human activity recognition is drawing escalating attention in recent years in both academia and industry due to the potentials in bracing such a broad range of Internet of Things (IoT) applications as health diagnosis, human-machine interactions, safety surveillance, and so on. Among many forms of sensing technologies, e.g., using cameras, wearable sensors, and RFIDs, WiFi-based activity recognition is of particular interest given its ubiquity, low cost, device-free experience, and low dependence. Generally, people's motions will affect the reflected WiFi signals and incur specific radio patterns. Through profiling these specific patterns, we are able to recognize the original activities. Many existing works have reported relatively good activity recognition performance in dedicated scenarios; yet their performance degrades much in the practical complex applications with various impact factors, such as the co-channel interference, spatial diversity, and diverse environments, making existing WiFi-based solutions far from being satisfactory. In this thesis, we aim to address the existing key challenges and develop accurate, reliable, and adaptive WiFi-based human activity recognition systems. We argue that the integration of advanced deep learning techniques into the activity recognition will bring new opportunities towards our goal. Along this end, we first propose CSAR, a channel selective activity recognition framework that conquers the channel quality problem by active channel hopping and channel combination. We then develop WiSDAR, which constructs multiple separated antenna pairs and obtains features from multiple spatial dimensions to solve the spatial diversity problem. We at last investigate the activity recognition in a more compact in-car scenario and present WiCAR, a WiFi-based in-car activity recognition framework that leverages domain adaptation to remove the environment-specific information in the received signals while retaining the activity-related features for adaptive recognition. We have conducted extensive evaluations and the performance results further demonstrate the superiority of our frameworks over the state-of-the-art solutions.
Big Data Analytics For Sensor Network Collected Intelligence
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Author : Hui-Huang Hsu
language : en
Publisher: Morgan Kaufmann
Release Date : 2017-02-02
Big Data Analytics For Sensor Network Collected Intelligence written by Hui-Huang Hsu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-02 with Computers categories.
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
Advances In Human Activity Detection And Recognition Hadr Systems
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Author : Santosh Kumar Tripathy
language : en
Publisher: Springer Nature
Release Date :
Advances In Human Activity Detection And Recognition Hadr Systems written by Santosh Kumar Tripathy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.