书目名称 | Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video |
编辑 | Olga Isupova |
视频video | |
概述 | 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 l |
丛书名称 | Springer Theses |
图书封面 |  |
描述 | .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 anovel 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 deve |
出版日期 | Book 2018 |
关键词 | Machine Learning; Intelligent Vision Systems; Dynamic Type Models; Behaviour Analysis; Anomaly Detection |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-319-75508-3 |
isbn_softcover | 978-3-030-09250-4 |
isbn_ebook | 978-3-319-75508-3Series ISSN 2190-5053 Series E-ISSN 2190-5061 |
issn_series | 2190-5053 |
copyright | Springer International Publishing AG 2018 |