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001 978-0-85729-670-2
003 DE-He213
005 20140220083714.0
007 cr nn 008mamaa
008 110525s2011 xxk| s |||| 0|eng d
020 _a9780857296702
_9978-0-85729-670-2
024 7 _a10.1007/978-0-85729-670-2
_2doi
050 4 _aT385
050 4 _aTA1637-1638
050 4 _aTK7882.P3
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
100 1 _aGong, Shaogang.
_eauthor.
245 1 0 _aVisual Analysis of Behaviour
_h[electronic resource] :
_bFrom Pixels to Semantics /
_cby Shaogang Gong, Tao Xiang.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2011.
300 _aXIX, 356p. 131 illus., 101 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart I: Introduction -- About Behaviour -- Behaviour in Context -- Towards Modelling Behaviour -- Part II: Single-Object Behaviour -- Understanding Facial Expression -- Modelling Gesture -- Action Recognition -- Part III: Group Behaviour -- Supervised Learning of Group Activity -- Unsupervised Behaviour Profiling -- Hierarchical Behaviour Discovery -- Learning Behavioural Context -- Modelling Rare and Subtle Behaviours -- Man in the Loop -- Part IV: Distributed Behaviour -- Multi-Camera Behaviour Correlation -- Person Re-Identification -- Connecting the Dots -- Epilogue.
520 _aDemand continues to grow worldwide, from both government and commerce, for technologies capable of automatically selecting and identifying object and human behaviour. This accessible text/reference presents a comprehensive and unified treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. The book provides in-depth discussion on computer vision and statistical machine learning techniques, in addition to reviewing a broad range of behaviour modelling problems. A mathematical background is not required to understand the content, although readers will benefit from modest knowledge of vectors and matrices, eigenvectors and eigenvalues, linear algebra, optimisation, multivariate analysis, probability, statistics and calculus. Topics and features: Provides a thorough introduction to the study and modelling of behaviour, and a concluding epilogue Covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning of behaviours Examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for global abnormal behaviour detection Discusses Bayesian information criterion, static Bayesian graph models, “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 Includes a helpful list of acronyms A valuable resource for both researchers in computer vision and machine learning, and for developers of commercial applications, the book can also serve as a useful reference for postgraduate students of computer science and behavioural science. Furthermore, policymakers and commercial managers will find this an informed guide on intelligent video analytics systems. Dr. Shaogang Gong is a Professor of Visual Computation in the School of Electronic Engineering and Computer Science at Queen Mary University of London, UK. Dr. Tao Xiang is a Lecturer at the same institution.
650 0 _aComputer science.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 0 _aBiometrics.
650 0 _aSocial sciences.
650 1 4 _aComputer Science.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aBiometrics.
650 2 4 _aPattern Recognition.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aSocial Sciences, general.
700 1 _aXiang, Tao.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780857296696
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-85729-670-2
912 _aZDB-2-SCS
999 _c105255
_d105255