Springer Theses

Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video

Authors: Isupova, Olga

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  • Nominated by the University of Sheffield as an outstanding Ph.D. thesis
  • Proposes statistical hypothesis tests for both offline and online data processing and multiple change-point detection
  • Develops learning algorithms for a dynamic topic model
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eBook 91,62 €
price for Spain (gross)
  • ISBN 978-3-319-75508-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-3-319-75507-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

Table of contents (6 chapters)

  • Introduction

    Isupova, Olga

    Pages 1-7

    Preview Buy Chapter 30,19 €
  • Background

    Isupova, Olga

    Pages 9-35

    Preview Buy Chapter 30,19 €
  • Proposed Learning Algorithms for Markov Clustering Topic Model

    Isupova, Olga

    Pages 37-64

    Preview Buy Chapter 30,19 €
  • Dynamic Hierarchical Dirichlet Process

    Isupova, Olga

    Pages 65-82

    Preview Buy Chapter 30,19 €
  • Change Point Detection with Gaussian Processes

    Isupova, Olga

    Pages 83-104

    Preview Buy Chapter 30,19 €

Buy this book

eBook 91,62 €
price for Spain (gross)
  • ISBN 978-3-319-75508-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 114,39 €
price for Spain (gross)
  • ISBN 978-3-319-75507-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video
Authors
Series Title
Springer Theses
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-75508-3
DOI
10.1007/978-3-319-75508-3
Hardcover ISBN
978-3-319-75507-6
Series ISSN
2190-5053
Edition Number
1
Number of Pages
XXV, 126
Number of Illustrations
2 b/w illustrations, 25 illustrations in colour
Topics