Statistical Machine Learning for Human Behaviour Analysis PDF Download
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Author: Thomas Moeslund
Publisher: MDPI
ISBN: 3039362283
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
Author: Thomas Moeslund
Publisher: MDPI
ISBN: 3039362283
Category : Technology & Engineering
Languages : en
Pages : 300
Get Book Here
Book Description
This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
Author: Enrique Garcia Ceja
Publisher: CRC Press
ISBN: 1000484254
Category : Psychology
Languages : en
Pages : 370
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Book Description
Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
Author: Longbing Cao
Publisher: Springer Science & Business Media
ISBN: 1447129695
Category : Computers
Languages : en
Pages : 371
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Book Description
'Behavior' is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence. With contributions from leading researchers in this emerging field Behavior Computing: Modeling, Analysis, Mining and Decision includes chapters on: representation and modeling behaviors; behavior ontology; behaviour analysis; behaviour pattern mining; clustering complex behaviors; classification of complex behaviors; behaviour impact analysis; social behaviour analysis; organizational behaviour analysis; and behaviour computing applications. Behavior Computing: Modeling, Analysis, Mining and Decision provides a dedicated source of reference for the theory and applications of behavior informatics and behavior computing. Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable.
Author: Yangsheng Xu
Publisher: CRC Press
ISBN: 1000654559
Category : Computers
Languages : en
Pages : 360
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Book Description
Bridging the gap between human-computer engineering and control engineering, Human Behavior Learning and Transfer delineates how to abstract human action and reaction skills into computational models. The authors include methods for modeling a variety of human action and reaction behaviors and explore processes for evaluating, optimizing, and trans
Author: Shaogang Gong
Publisher: Springer Science & Business Media
ISBN: 0857296701
Category : Computers
Languages : en
Pages : 358
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Book Description
This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning; examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Olga Isupova
Publisher: Springer
ISBN: 3319755080
Category : Technology & Engineering
Languages : en
Pages : 144
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Book Description
This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes. Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed. The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.
Author: Jerry J. Buccafusco
Publisher: CRC Press
ISBN: 1420041819
Category : Medical
Languages : en
Pages : 341
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Book Description
Using the most well-studied behavioral analyses of animal subjects to promote a better understanding of the effects of disease and the effects of new therapeutic treatments on human cognition, Methods of Behavior Analysis in Neuroscience provides a reference manual for molecular and cellular research scientists in both academia and the pharmaceutic
Author: Md Zia Uddin
Publisher: CRC Press
ISBN: 1040105467
Category : Computers
Languages : en
Pages : 264
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Book Description
This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field. Key Features: Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journey Striking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchers The content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalities Covering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications
Author: Zhiwen Yu
Publisher: Springer Nature
ISBN: 9811521093
Category : Computers
Languages : en
Pages : 277
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Book Description
Over the last decade, there has been a growing interest in human behavior analysis, motivated by societal needs such as security, natural interfaces, affective computing, and assisted living. However, the accurate and non-invasive detection and recognition of human behavior remain major challenges and the focus of many research efforts. Traditionally, in order to identify human behavior, it is first necessary to continuously collect the readings of physical sensing devices (e.g., camera, GPS, and RFID), which can be worn on human bodies, attached to objects, or deployed in the environment. Afterwards, using recognition algorithms or classification models, the behavior types can be identified so as to facilitate advanced applications. Although such traditional approaches deliver satisfactory performance and are still widely used, most of them are intrusive and require specific sensing devices, raising issues such as privacy and deployment costs. In this book, we will present our latest findings on non-invasive sensing and understanding of human behavior. Specifically, this book differs from existing literature in the following senses. Firstly, we focus on approaches that are based on non-invasive sensing technologies, including both sensor-based and device-free variants. Secondly, while most existing studies examine individual behaviors, we will systematically elaborate on how to understand human behaviors of various granularities, including not only individual-level but also group-level and community-level behaviors. Lastly, we will discuss the most important scientific problems and open issues involved in human behavior analysis.
