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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.
List of contents
Chapter 1 Introduction.- Chapter 2 Mathematical Preliminaries for Video Anomaly Detection Techniques.- Chapter 3 Probability Based Video Anomaly Detection Approaches.- Chapter 4 k-Nearest Neighbor Based Video Anomaly Detection Approaches.- Chapter 5 Gaussian Mixture Model Based Video Anomaly Detection.
About the author
Xiaochun Wang received her B.S. degree from Beijing University and her M.S. and Ph.D. from the Department of Electrical Engineering and Computer Science at Vanderbilt University. She is now Senior Scientist in Xi'an Tuowei High-Tech Corporation. Before this, she worked in the School of Software Engineering, Xi'an Jiaotong University in China. Her research interests include data mining (particularly, outlier detection and clustering), machine learning, computer vision, signal processing, and pattern recognition.
