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007			cr nn 008mamaa
008			180224s2018 gw | s |||| 0|eng d
020			_a9783319755083
024	7		_a10.1007/978-3-319-75508-3 _2doi
040			_bspa _dES-MaUEC
050		4	_aQ325.5
100	1		_aIsupova, Olga. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/215348811/
245	1	0	_aMachine Learning Methods for Behaviour Analysis and Anomaly Detection in Video _cby Olga Isupova.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXV, 126 páginas 27 ilustraciones, 25 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5053
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Background -- Proposed Learning Algorithms for Markov Clustering Topic Model -- Dynamic Hierarchical Dirlchlet Process -- Change Point Detection with Gaussian Processes -- Conclusions and Future Work.
520	3		_aThis 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.
988			_aEBSPRINGER_2018
650		7	_aAprendizaje automático _2embne _9166090
776	0	8	_iEdición impresa: _z9783319755076
776	0	8	_iEdición impresa: _z9783319755090
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-75508-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2019 _dz _zSI _eIG