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

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

Label
Machine learning methods for behaviour analysis and anomaly detection in video
Title
Machine learning methods for behaviour analysis and anomaly detection in video
Statement of responsibility
Olga Isupova
Creator
Author
Subject
Language
eng
Summary
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
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Isupova, Olga
Dewey number
006.3/1
Illustrations
illustrations
Index
no index present
LC call number
Q325.5
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Springer theses,
http://library.link/vocab/subjectName
  • Machine learning
  • Anomaly detection (Computer security)
  • Engineering
  • Signal, Image and Speech Processing
  • Image Processing and Computer Vision
  • Artificial Intelligence (incl. Robotics)
  • Computational Intelligence
Label
Machine learning methods for behaviour analysis and anomaly detection in video, Olga Isupova
Instantiates
Publication
Note
"Doctoral thesis accepted by the University of Sheffield, Sheffield, UK."
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- Background -- Proposed Learning Algorithms for Markov Clustering Topic Model -- Dynamic Hierarchical Dirlchlet Process -- Change Point Detection with Gaussian Processes -- Conclusions and Future Work
Control code
SPR1025329866
Dimensions
unknown
Extent
1 online resource (xxv, 126 pages)
File format
unknown
Form of item
online
Isbn
9783319755083
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-75508-3
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1025329866
  • (OCoLC)1025329866
Label
Machine learning methods for behaviour analysis and anomaly detection in video, Olga Isupova
Publication
Note
"Doctoral thesis accepted by the University of Sheffield, Sheffield, UK."
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- Background -- Proposed Learning Algorithms for Markov Clustering Topic Model -- Dynamic Hierarchical Dirlchlet Process -- Change Point Detection with Gaussian Processes -- Conclusions and Future Work
Control code
SPR1025329866
Dimensions
unknown
Extent
1 online resource (xxv, 126 pages)
File format
unknown
Form of item
online
Isbn
9783319755083
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-75508-3
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1025329866
  • (OCoLC)1025329866

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