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100	1		_aWang, Xiaochun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aAnomaly Detection in Video Surveillance _h[electronic resource] / _cby Xiaochun Wang.
250			_a1st ed. 2024.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024.
300			_aXIX, 384 p. 137 illus., 90 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aCognitive Intelligence and Robotics, _x2520-1964
505	0		_aChapter 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.
520			_aAnomaly 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.
541			_fUABC ; _cPerpetuidad
650		0	_aComputer vision.
650		0	_aData mining.
650		0	_aImage processing _xDigital techniques.
650		0	_aMachine learning.
650		0	_aPattern recognition systems.
650		0	_aComputer science.
650	1	4	_aComputer Vision.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aMachine Learning.
650	2	4	_aAutomated Pattern Recognition.
650	2	4	_aTheory and Algorithms for Application Domains.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789819730223
776	0	8	_iPrinted edition: _z9789819730247
776	0	8	_iPrinted edition: _z9789819730254
830		0	_aCognitive Intelligence and Robotics, _x2520-1964
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-3023-0
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cLIBRO_ELEC
999			_c275954 _d275953