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Machine learning methods for behavio...

Isupova, Olga.

Machine learning methods for behaviour analysis and anomaly detection in video

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning methods for behaviour analysis and anomaly detection in videoby Olga Isupova.
Author:	Isupova, Olga.
Published:	Cham :Springer International Publishing :2018.
Description:	xxv, 126 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-75508-3
ISBN:	9783319755083$q(electronic bk.)

Machine learning methods for behaviour analysis and anomaly detection in video
Isupova, Olga.

Machine learning methods for behaviour analysis and anomaly detection in video[electronic resource] /by Olga Isupova. - Cham :Springer International Publishing :2018. - xxv, 126 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Background -- Proposed Learning Algorithms for Markov Clustering Topic Model -- Dynamic Hierarchical Dirlchlet Process -- Change Point Detection with Gaussian Processes -- Conclusions and Future Work.

This 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.

ISBN: 9783319755083$q(electronic bk.)

Standard No.: 10.1007/978-3-319-75508-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning methods for behaviour analysis and anomaly detection in video
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505 0 $a Introduction -- Background -- Proposed Learning Algorithms for Markov Clustering Topic Model -- Dynamic Hierarchical Dirlchlet Process -- Change Point Detection with Gaussian Processes -- Conclusions and Future Work.
520 $a This 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.
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