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Author: Tian Wang Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
One of the major research areas in computer vision is visual surveillance. The scientific challenge in this area includes the implementation of automatic systems for obtaining detailed information about the behavior of individuals and groups. Particularly, detection of abnormal individual movements requires sophisticated image analysis. This thesis focuses on the problem of the abnormal events detection, including feature descriptor design characterizing the movement information and one-class kernel-based classification methods. In this thesis, three different image features have been proposed: (i) global optical flow features, (ii) histograms of optical flow orientations (HOFO) descriptor and (iii) covariance matrix (COV) descriptor. Based on these proposed descriptors, one-class support vector machines (SVM) are proposed in order to detect abnormal events. Two online strategies of one-class SVM are proposed: The first strategy is based on support vector description (online SVDD) and the second strategy is based on online least squares one-class support vector machines (online LS-OC-SVM).
Author: Tian Wang Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
One of the major research areas in computer vision is visual surveillance. The scientific challenge in this area includes the implementation of automatic systems for obtaining detailed information about the behavior of individuals and groups. Particularly, detection of abnormal individual movements requires sophisticated image analysis. This thesis focuses on the problem of the abnormal events detection, including feature descriptor design characterizing the movement information and one-class kernel-based classification methods. In this thesis, three different image features have been proposed: (i) global optical flow features, (ii) histograms of optical flow orientations (HOFO) descriptor and (iii) covariance matrix (COV) descriptor. Based on these proposed descriptors, one-class support vector machines (SVM) are proposed in order to detect abnormal events. Two online strategies of one-class SVM are proposed: The first strategy is based on support vector description (online SVDD) and the second strategy is based on online least squares one-class support vector machines (online LS-OC-SVM).
Author: Seby Jacob Publisher: ISBN: Category : Languages : en Pages :
Book Description
"This thesis proposes an innovative solution to detect and localize anomalous events in a video stream from a static camera. Anomalies are defined as events with a very low probability of occurrence in the scene or as events typically uncharacteristic of the scene. In this work, we employ a constrained convolutional auto-encoder to learn the scene characteristics. The autoencoder is trained on spatio-temporal video-volumes extracted from recorded videos of the scene. Once the training is complete, each incoming video-volume can be tested for its anomalous nature by analyzing the low-dimensional encodings and the quality of its reconstruction from the auto-encoder. Anomalies are heavily subjective to the scene being monitored. The most abnormal event in one scene could be the most normal event in another. Hence, special care has been taken to make the solution applicable for any scenario. Since training is unsupervised, this work is extremely general purpose and can be deployed on any scene as is. Apart from the discourse on a novel solution that is competitive with state-of-the-art methods, this work also has an additional contribution. Specifically, we present a framework for generating unlimited amounts of video data for anomaly detection from a static camera. This enables the evaluation of any deep learning models, that were previously not adaptable for the problem due to the limited training data available in benchmark datasets. We present results from extensive experimentation on popular benchmark datasets to show that our solution is effective and robust for anomaly detection. We also establish the importance of having sufficient training data via the evaluation of models trained on training- sets of varying sizes. Finally, the idiosyncratic nature of "What is an anomaly?" is subjected to analysis using an experimental methodology." --
Author: Patrick Schneider Publisher: Academic Press ISBN: 0128238194 Category : Computers Languages : en Pages : 408
Book Description
Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. - Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge - Covers extraction (Anomaly Detection) - Illustrates new, scalable and reliable processing techniques based on IoT stream technologies - Offers applications to new, real-time anomaly detection scenarios in the health domain
Author: Radek Silhavy Publisher: Springer Nature ISBN: 303109073X Category : Technology & Engineering Languages : en Pages : 628
Book Description
This book contains the refereed proceedings of the Cybernetics Perspectives in Systems session of the 11th Computer Science On-line Conference 2022 (CSOC 2022), which was held in April 2022 online. Papers on modern cybernetics and informatics in the context of networks and systems are an important component of current research issues. This volume contains an overview of recent method, algorithms and designs.
Author: Plamen P. Angelov Publisher: Springer ISBN: 3030023842 Category : Technology & Engineering Languages : en Pages : 437
Book Description
This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code. Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.” Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.” Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”
Author: Shai Avidan Publisher: Springer Nature ISBN: 303119778X Category : Computers Languages : en Pages : 804
Book Description
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Esra Negris Yolacan Publisher: ISBN: Category : Anomaly detection (Computer security) Languages : en Pages : 141
Book Description
Anomaly detection has been used in a wide range of real world problems and has received significant attention in a number of research fields over the last decades. Anomaly detection attempts to identify events, activities, or observations which are measurably different than an expected behavior or pattern present in a dataset. This thesis focuses on a specific set of techniques targeting the detection of anomalous behavior in a discrete, symbolic, and sequential dataset. Since profiling complex sequential data is still an open problem in anomaly detection, and given that the rate of production of sequential data in fields ranging from finance to homeland security is exploding, there is a pressing need to develop effective detection algorithms that can handle patterns in sequential information flows. In this thesis, we address context-aware multi-class anomaly detection as applied to discrete sequences and develop a context learning approach using an unsupervised learning paradigm. We begin the anomaly detection process by applying our approach to differentiate normal behavior classes (contexts) before attempting to model normal behavior. This approach leads to stronger learning on each class by taking advantage of the power of advanced models to identify normal behavior of the sequence classes. We evaluate our discrete sequence-based anomaly detection framework using two illustrative applications: 1) System call intrusion detection and 2) Crowd anomaly detection. We also evaluate how clustering can guide our context-aware methodology to positively impact the anomaly detection rate. In this thesis, we utilize a Hidden Markov Model (HMM) to perform anomaly detection. A HMM is the simplest dynamic Bayesian network. A HMM is a Markov model which can be used when the states are not observable, but observed data is dependent on these hidden states. While there has been a large amount of prior work utilizing Hidden Markov Models (HMMs) for anomaly detection, the proposed models became overly complex when attempting to improve the detection rate, while reducing the false detection rate. We apply HMMs to perform anomaly detection on discrete sequential data. We utilize multiple HMMs, one for each context class. We demonstrate our multi-HMM approach to system call anomalies in cyber security and provide results in the presence of anomalies. Applying process trace analysis with multi-HMMs, system call anomaly detection achieves better results using better tuned model settings and a less complex structure to detect anomalies. To evaluate the extensibility of our approach, we consider a second application, crowd behavior analytics. We attempt to classify crowd behavior and treat this as an anomaly detection problem on sequential data. We convert crowd video data into a discrete/symbolic sequence of data. We apply computer vision techniques to generate features from objects, and use these features for frame-based representations to model the behavior of the crowd in a video stream. We attempt to identify anomalous behavior of a crowd in a scene by applying machine learning techniques to understand what it means for a video stream to be identified as "normal". The results of applying our context-aware multi-HMMs approach to crowd analytics show the generality of our anomaly detection approach, and the power of our context-learning approach.
Author: Charu C. Aggarwal Publisher: Springer ISBN: 3319547658 Category : Computers Languages : en Pages : 288
Book Description
This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.
Author: Andrea Fusiello Publisher: Springer ISBN: 3642338852 Category : Computers Languages : en Pages : 703
Book Description
The three volume set LNCS 7583, 7584 and 7585 comprises the Workshops and Demonstrations which took place in connection with the European Conference on Computer Vision, ECCV 2012, held in Firenze, Italy, in October 2012. The total of 179 workshop papers and 23 demonstration papers was carefully reviewed and selected for inclusion in the proceedings. They where held at workshops with the following themes: non-rigid shape analysis and deformable image alignment; visual analysis and geo-localization of large-scale imagery; Web-scale vision and social media; video event categorization, tagging and retrieval; re-identification; biological and computer vision interfaces; where computer vision meets art; consumer depth cameras for computer vision; unsolved problems in optical flow and stereo estimation; what's in a face?; color and photometry in computer vision; computer vision in vehicle technology: from earth to mars; parts and attributes; analysis and retrieval of tracked events and motion in imagery streams; action recognition and pose estimation in still images; higher-order models and global constraints in computer vision; information fusion in computer vision for concept recognition; 2.5D sensing technologies in motion: the quest for 3D; benchmarking facial image analysis technologies.
Author: Tian Wang
Author: Tian Wang
Author: Seby Jacob
Author: Xiaochun Wang
Author: Patrick Schneider
Author: Radek Silhavy
Author: Plamen P. Angelov
Author: Shai Avidan
Author: Esra Negris Yolacan
Author: Charu C. Aggarwal
Author: Andrea Fusiello