Visual Analysis of Behaviour von Shaogang Gong | From Pixels to Semantics | ISBN 9780857296696

Visual Analysis of Behaviour

From Pixels to Semantics

von Shaogang Gong und Tao Xiang
Mitwirkende
Autor / AutorinShaogang Gong
Autor / AutorinTao Xiang
Buchcover Visual Analysis of Behaviour | Shaogang Gong | EAN 9780857296696 | ISBN 0-85729-669-8 | ISBN 978-0-85729-669-6

From the reviews:

“The book aims to describe, analyze, and present the problem of visual analysis of behaviour of objects, with an emphasis on behaviour of people. I have thoroughly enjoyed going through the book and find it a strong contribution to the area of computer vision and automated visual analysis. … The book is well suited for academics and researchers … and would be a great resource for implementing any of the algorithms described therein.” (Elena Corina Grigore, Perception, Vol. 42, 2013)

“This book presents a comprehensive introduction to algorithms and methodologies for representing, learning, recognizing, interpreting and predicting human behaviour, on the basis of visual data. Examples of human behaviour are given by facial expression, body gesture and human action. The book is mainly intended for readers interested in applications in the fields of visual surveillance, video indexing and search, robotics and healthcare, interaction, animation and computer games.” (Patrizio Frosini, Zentralblatt MATH, Vol. 1238, 2012)

Visual Analysis of Behaviour

From Pixels to Semantics

von Shaogang Gong und Tao Xiang
Mitwirkende
Autor / AutorinShaogang Gong
Autor / AutorinTao Xiang
This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning; examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines.