000 | 04712nam a22006375i 4500 | ||
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001 | 978-3-319-75508-3 | ||
003 | DE-He213 | ||
005 | 20210201191518.0 | ||
007 | cr nn 008mamaa | ||
008 | 180224s2018 gw | s |||| 0|eng d | ||
020 |
_a9783319755083 _9978-3-319-75508-3 |
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050 | 4 | _aTK5102.9 | |
050 | 4 | _aTA1637-1638 | |
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_aTTBM _2thema |
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_aUYS _2thema |
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_a621.382 _223 |
100 | 1 |
_aIsupova, Olga. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXXV, 126 p. 27 illus., 25 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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. |
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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 |