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020 _a9783319755083
_9978-3-319-75508-3
050 4 _aTK5102.9
050 4 _aTA1637-1638
072 7 _aTTBM
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_2bisacsh
072 7 _aTTBM
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621.382
_223
100 1 _aIsupova, Olga.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning Methods for Behaviour Analysis and Anomaly Detection in Video
_h[electronic resource] /
_cby Olga Isupova.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXXV, 126 p. 27 illus., 25 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 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
500 _aAcceso multiusuario
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 _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.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aSignal processing.
650 0 _aImage processing.
650 0 _aSpeech processing systems.
650 0 _aOptical data processing.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aSignal, Image and Speech Processing.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T24051
650 2 4 _aImage Processing and Computer Vision.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I22021
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319755076
776 0 8 _iPrinted edition:
_z9783319755090
776 0 8 _iPrinted edition:
_z9783030092504
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-75508-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c244323
_d244322