Machine Learning Methods For Behaviour Analysis And Anomaly Detection In Video

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Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video

Olga Isupova
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video Book PDF

Author : Olga Isupova
Publisher : Springer
Page : 126 pages
File Size : 50,6 Mb
Release : 2018-02-24
Category : Technology & Engineering
ISBN : 9783319755083

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Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video by Olga Isupova Pdf

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.

Behavior Analysis with Machine Learning Using R

Enrique Garcia Ceja
Behavior Analysis with Machine Learning Using R Book PDF

Author : Enrique Garcia Ceja
Publisher : CRC Press
Page : 434 pages
File Size : 44,7 Mb
Release : 2021-11-26
Category : Psychology
ISBN : 9781000484236

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Behavior Analysis with Machine Learning Using R by Enrique Garcia Ceja Pdf

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.

Anomaly Detection in Video Surveillance

Xiaochun Wang
Anomaly Detection in Video Surveillance Book PDF

Author : Xiaochun Wang
Publisher : Springer
Page : 0 pages
File Size : 50,6 Mb
Release : 2024-07-28
Category : Computers
ISBN : 9819730228

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Anomaly Detection in Video Surveillance by Xiaochun Wang Pdf

Anomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value. The key advantage of writing the book at this point in time is that the vast amount of work done by computer scientists over the last few decades has remained largely untouched by a formal book on the subject, although these techniques significantly advance existing methods of image and video analysis and understanding by taking advantage of anomaly detection in the data mining community and visual analysis in the computer vision community. The proposed book provides a comprehensive coverage of the advances in video based anomaly detection, including topics such as the theories of anomaly detection and machine perception for the functional analysis of abnormal events in general, the identification of abnormal behaviour and crowd abnormal behaviour in particular, the current understanding of computer vision development, and the application of this present understanding towards improving video-based anomaly detection in theory and coding with OpenCV. The book also provides a perspective on deep learning on human action recognition and behaviour analysis, laying the groundwork for future advances in these areas. Overall, the chapters of this book have been carefully organized with extensive bibliographic notes attached to each chapter. One of the goals is to provide the first systematic and comprehensive description of the range of data-driven solutions currently being developed up to date for such purposes. Another is to serve a dual purpose so that students and practitioners can use it as a textbook while researchers can use it as a reference book. A final goal is to provide a comprehensive exposition of the topic of anomaly detection in video media from multiple points of view.

Intelligent Image and Video Analytics

El-Sayed M. El-Alfy,George Bebis,Mengchu Zhou
Intelligent Image and Video Analytics Book PDF

Author : El-Sayed M. El-Alfy,George Bebis,Mengchu Zhou
Publisher : CRC Press
Page : 404 pages
File Size : 45,5 Mb
Release : 2023-04-12
Category : Computers
ISBN : 9781000851915

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Intelligent Image and Video Analytics by El-Sayed M. El-Alfy,George Bebis,Mengchu Zhou Pdf

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications. Features: Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics. Explores important applications that require techniques from both artificial intelligence and computer vision. Describes multimodality video analytics for different applications. Examines issues related to multimodality data fusion and highlights research challenges. Integrates various techniques from video processing, data mining and machine learning which has many emerging indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities. This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

Policing in the Era of AI and Smart Societies

Hamid Jahankhani,Babak Akhgar,Peter Cochrane,Mohammad Dastbaz
Policing in the Era of AI and Smart Societies Book PDF

Author : Hamid Jahankhani,Babak Akhgar,Peter Cochrane,Mohammad Dastbaz
Publisher : Springer Nature
Page : 282 pages
File Size : 42,7 Mb
Release : 2020-07-17
Category : Social Science
ISBN : 9783030506131

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Policing in the Era of AI and Smart Societies by Hamid Jahankhani,Babak Akhgar,Peter Cochrane,Mohammad Dastbaz Pdf

Chapter “Predictive Policing in 2025: A Scenario” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Machine Learning in Intrusion Detection

Yihua Liao
Machine Learning in Intrusion Detection Book PDF

Author : Yihua Liao
Publisher : Unknown
Page : 230 pages
File Size : 55,5 Mb
Release : 2005
Category : Electronic
ISBN : UCAL:X70931

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Machine Learning in Intrusion Detection by Yihua Liao Pdf

Detection of anomalies in data is one of the fundamental machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate that our approach is able to effectively detect intrusive program behavior while a low false positive rate is achieved. Second, we describe an adaptive anomaly detection framework that is de- signed to handle concept drift and online learning for dynamic, changing environments. Through the use of unsupervised evolving connectionist systems, normal behavior changes are efficiently accommodated while anomalous activities can still be recognized. We demonstrate the performance of our adaptive anomaly detection systems and show that the false positive rate can be significantly reduced.

Statistical Machine Learning for Human Behaviour Analysis

Thomas Moeslund,Sergio Escalera,Gholamreza Anbarjafari,Kamal Nasrollahi,Jun Wan
Statistical Machine Learning for Human Behaviour Analysis Book PDF

Author : Thomas Moeslund,Sergio Escalera,Gholamreza Anbarjafari,Kamal Nasrollahi,Jun Wan
Publisher : MDPI
Page : 300 pages
File Size : 44,8 Mb
Release : 2020-06-17
Category : Technology & Engineering
ISBN : 9783039362288

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Statistical Machine Learning for Human Behaviour Analysis by Thomas Moeslund,Sergio Escalera,Gholamreza Anbarjafari,Kamal Nasrollahi,Jun Wan Pdf

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.

Network Anomaly Detection

Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Network Anomaly Detection Book PDF

Author : Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 366 pages
File Size : 43,5 Mb
Release : 2013-06-18
Category : Computers
ISBN : 9781466582095

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Network Anomaly Detection by Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita Pdf

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

Handbook of Research on Innovation and Development of E-Commerce and E-Business in ASEAN

Almunawar, Mohammad Nabil,Anshari Ali, Muhammad,Ariff Lim, Syamimi
Handbook of Research on Innovation and Development of E Commerce and E Business in ASEAN Book PDF

Author : Almunawar, Mohammad Nabil,Anshari Ali, Muhammad,Ariff Lim, Syamimi
Publisher : IGI Global
Page : 883 pages
File Size : 52,7 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781799849858

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Handbook of Research on Innovation and Development of E-Commerce and E-Business in ASEAN by Almunawar, Mohammad Nabil,Anshari Ali, Muhammad,Ariff Lim, Syamimi Pdf

Business-to-consumer (B2C) and consumer-to-consumer (C2C) e-commerce transactions, including social commerce, are rapidly expanding, although e-commerce is still small when compared to traditional business transactions. As the familiarity of making purchases using smart devices continues to expand, many global and regional investors hope to target the ASEAN region to tap into the rising digital market in this region. The Handbook of Research on Innovation and Development of E-Commerce and E-Business in ASEAN is an essential reference source that discusses economics, marketing strategies, and mobile payment systems, as well as digital marketplaces, communication technologies, and social technologies utilized for business purposes. Featuring research on topics such as business culture, mobile technology, and consumer satisfaction, this book is ideally designed for policymakers, financial managers, business professionals, academicians, students, and researchers.

Artificial Intelligence and Machine Learning for EDGE Computing

Rajiv Pandey,Sunil Kumar Khatri,Neeraj Kumar Singh,Parul Verma
Artificial Intelligence and Machine Learning for EDGE Computing Book PDF

Author : Rajiv Pandey,Sunil Kumar Khatri,Neeraj Kumar Singh,Parul Verma
Publisher : Academic Press
Page : 516 pages
File Size : 48,8 Mb
Release : 2022-04-26
Category : Science
ISBN : 9780128240557

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Artificial Intelligence and Machine Learning for EDGE Computing by Rajiv Pandey,Sunil Kumar Khatri,Neeraj Kumar Singh,Parul Verma Pdf

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints

Applied Cloud Deep Semantic Recognition

Mehdi Roopaei,Peyman Najafirad (Paul Rad)
Applied Cloud Deep Semantic Recognition Book PDF

Author : Mehdi Roopaei,Peyman Najafirad (Paul Rad)
Publisher : CRC Press
Page : 239 pages
File Size : 51,6 Mb
Release : 2018-04-09
Category : Computers
ISBN : 9781351119009

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Applied Cloud Deep Semantic Recognition by Mehdi Roopaei,Peyman Najafirad (Paul Rad) Pdf

This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which research has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.

Computer-Aided Developments: Electronics and Communication

Arun Kumar Sinha,John Pradeep Darsy
Computer Aided Developments Electronics and Communication Book PDF

Author : Arun Kumar Sinha,John Pradeep Darsy
Publisher : CRC Press
Page : 282 pages
File Size : 45,5 Mb
Release : 2019-09-30
Category : Technology & Engineering
ISBN : 9781000751758

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Computer-Aided Developments: Electronics and Communication by Arun Kumar Sinha,John Pradeep Darsy Pdf

The volume comprises of papers presented at the first CADEC-2019 conference held at Vellore Institute of Technology-Andhra Pradesh, Amaravati, India. The book contains computer simulated results in various areas of electronics and communication engineering such as, VLSI and embedded systems, wireless communication, signal processing, power electronics and control theory applications. This volume will help researchers and engineers to develop and extend their ideas in upcoming research in electronics and communication.

Information and Communication Technology for Competitive Strategies (ICTCS 2020)

Amit Joshi,Mufti Mahmud,Roshan G. Ragel,Nileshsingh V. Thakur
Information and Communication Technology for Competitive Strategies ICTCS 2020 Book PDF

Author : Amit Joshi,Mufti Mahmud,Roshan G. Ragel,Nileshsingh V. Thakur
Publisher : Springer Nature
Page : 1128 pages
File Size : 55,7 Mb
Release : 2021-07-26
Category : Technology & Engineering
ISBN : 9789811607394

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Information and Communication Technology for Competitive Strategies (ICTCS 2020) by Amit Joshi,Mufti Mahmud,Roshan G. Ragel,Nileshsingh V. Thakur Pdf

This book contains the best selected research papers presented at ICTCS 2020: Fifth International Conference on Information and Communication Technology for Competitive Strategies. The conference was held at Jaipur, Rajasthan, India, during 11–12 December 2020. The book covers state-of-the-art as well as emerging topics pertaining to ICT and effective strategies for its implementation for engineering and managerial applications. This book contains papers mainly focused on ICT for computation, algorithms and data analytics, and IT security.

Learning to Analyze what is Beyond the Visible Spectrum

Amanda Berg
Learning to Analyze what is Beyond the Visible Spectrum Book PDF

Author : Amanda Berg
Publisher : Linköping University Electronic Press
Page : 111 pages
File Size : 40,8 Mb
Release : 2019-11-13
Category : Electronic
ISBN : 9789179299811

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Learning to Analyze what is Beyond the Visible Spectrum by Amanda Berg Pdf

Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing camera price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as there exists a measurable temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. This thesis addresses the problem of automatic image analysis in thermal infrared images with a focus on machine learning methods. The main purpose of this thesis is to study the variations of processing required due to the thermal infrared data modality. In particular, three different problems are addressed: visual object tracking, anomaly detection, and modality transfer. All these are research areas that have been and currently are subject to extensive research. Furthermore, they are all highly relevant for a number of different real-world applications. The first addressed problem is visual object tracking, a problem for which no prior information other than the initial location of the object is given. The main contribution concerns benchmarking of short-term single-object (STSO) visual object tracking methods in thermal infrared images. The proposed dataset, LTIR (Linköping Thermal Infrared), was integrated in the VOT-TIR2015 challenge, introducing the first ever organized challenge on STSO tracking in thermal infrared video. Another contribution also related to benchmarking is a novel, recursive, method for semi-automatic annotation of multi-modal video sequences. Based on only a few initial annotations, a video object segmentation (VOS) method proposes segmentations for all remaining frames and difficult parts in need for additional manual annotation are automatically detected. The third contribution to the problem of visual object tracking is a template tracking method based on a non-parametric probability density model of the object's thermal radiation using channel representations. The second addressed problem is anomaly detection, i.e., detection of rare objects or events. The main contribution is a method for truly unsupervised anomaly detection based on Generative Adversarial Networks (GANs). The method employs joint training of the generator and an observation to latent space encoder, enabling stratification of the latent space and, thus, also separation of normal and anomalous samples. The second contribution is the previously unaddressed problem of obstacle detection in front of moving trains using a train-mounted thermal camera. Adaptive correlation filters are updated continuously and missed detections of background are treated as detections of anomalies, or obstacles. The third contribution to the problem of anomaly detection is a method for characterization and classification of automatically detected district heat leakages for the purpose of false alarm reduction. Finally, the thesis addresses the problem of modality transfer between thermal infrared and visual spectrum images, a previously unaddressed problem. The contribution is a method based on Convolutional Neural Networks (CNNs), enabling perceptually realistic transformations of thermal infrared to visual images. By careful design of the loss function the method becomes robust to image pair misalignments. The method exploits the lower acuity for color differences than for luminance possessed by the human visual system, separating the loss into a luminance and a chrominance part.

Machine Learning for Vision-Based Motion Analysis

Liang Wang,Guoying Zhao,Li Cheng,Matti Pietikäinen
Machine Learning for Vision Based Motion Analysis Book PDF

Author : Liang Wang,Guoying Zhao,Li Cheng,Matti Pietikäinen
Publisher : Springer Science & Business Media
Page : 372 pages
File Size : 45,8 Mb
Release : 2010-11-18
Category : Computers
ISBN : 9780857290571

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Machine Learning for Vision-Based Motion Analysis by Liang Wang,Guoying Zhao,Li Cheng,Matti Pietikäinen Pdf

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.