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BIBLIOGRAFÍABIBLIOGRAFÍA[DMB08][ES08]Tim Ellis Dimitrios Makris and James Black. Intelligent Visual Surveillance: TowardsCognitive Vision Systems. The Open Cybernetics and Systemics Journal., 2008.A.G.A. Perera. E. Swears, A. Hoogs. Learning Motion Patterns in Surveillance Videousing HMM Clustering. 2008.[ESP] Anthony Hoogs Eran Swears and A.G. Amitha Perera. HMM-Based Clustering forLearning Motion Patterns in Surveillance Video.[Fer96][FJ02]Cohn A G Hogg-D C. Fernyhough, J H. Generation of semantic regions from imagesequences. 1996.M. Figueiredo and A. Jain. Unsupervised learning of finite mixture models. PAMI,IEEE Transactions, 2002.[FM98] R. Fraile and S. J. Maybank. ehicle Trajectory Approximation and Classification. 1998.[HB92] R J Howarth and H Buxton. Analogical representation of spatial events forunderstanding traffic behaviour. 1992.[Hea08] Venetianer P. L. Lipton A. Hearing, N. The evolution of video surveillance:anoverview». Machine Vision and Applications. 2008.[JA11][JEM04][J.F00][JH95][JH96]J.J. Castro-Schez-L. Jimenez-Linares. J. Albusac, D. Vallejo. OCULUS surveillancesystem: Fuzzy on-line speed analysis from 2D images. 2011.Anthony J. Tromba Jerrold E. Marsden. Cálculo Vectorial; Quinta Edición. PearsonEducation S.A., 2004.D.C.Hogg. J.Fernyhough, A.G. Cohn. Constructing Qualitative Event Model Automaticallyfrom Video Input; Image and Vision Computing. 2000.N Johnson and D C Hogg. Learning the Distribution of Object Trajectories for EventRecognition. 1995.N. Johnson and D. C. Hogg. Learning the Distribution of Object Trajectories for EventRecognition. Image and Vision Computing. 1996.[JLH02] Tieniu Tan Jiangung Lou, Qifeng Liu and Weiming Hu. Semantic Interpretation ofObject Activities in a Surveillance System. 2002.[JMSV04] Alexandre Bernardino José Melo, Andrew Naftel and José Santos-Victor. RETRIEVALOF VEHICLE TRAJECTORIES AND ESTIMATION OF LANE GEOMETRYUSING NON-STATIONARY TRAFFIC SURVEILLANCE CAMERAS. University ofManchester Institute of Science & Technology, Manchester M60 1QD, UK and InstitutoSuperior Técnico, Av. Rovisco Pais 1049 - 001 Lisboa, Portugal, 2004.230
BIBLIOGRAFÍABIBLIOGRAFÍA[Joh98] N. Johnson. Learning Object Behaviour Models; PhD thesis, School of ComputerStudies. 1998.[JP04][Kal05][KB04]Jian Pei-M. Behzad. J. Pei, J. Han. Mining Sequential Patterns by Pattern-Growth: ThePrefixSpan Approach. 2004.y S. Bakiras. Kalnis, N. Mamoulis. On Discovering Moving Clusters in Spatio-temporalData. 2005.D.R. Anderson. K.P. Burnham. Multimodel inference: Understanding AIC and BIC inmodel selection. 2004.[Lut09] Mark Lutz. Learning Python, Third Edition. O’Reilly., 2009.[MAF81][ME]R. C. Bolles. M. A. Fischler. Random Sample Consensus: A Paradigm for Model Fittingwith Applications to Image Analysis and Automated Cartography. Comm. of the ACM.,1981.Dimitrios Makris and Tim Ellis. Path Detection in Video Surveillance.[N.98] Johnson N. Learning Object Behaviour Models. 1998.[NC08]Fredrik Nilsson and Axis Communications. Intelligent Network Video: UnderstandingModern Video Surveillance Systems. CRC Press - 416 Pages, 2008.[NS00] A. Bulpitt. N. Sumpter. Learning Spato-temporal Patterns for Predicting ObjectBehaviour, Image and Vision Computing. 2000.[PJS]David H. Eberly Philip J. Schneider. Geometric Tools for Computer Graphics. MorganKaufmann Publishers.[SB98] N Sumpter and J Bulpitt. Learning spatio-temporal patterns for predicting objectbehaviour. 1998.[Shr09][Smi04]Dave. Shreiner. OpenGL programming Guide:the official guide to learning OpenGL,versions 3.0 and 3.1, Seventh edition. Pearson Education, Inc., 2009.G. J. D. Smith. Behind the screens: Examining constructions of deviance and informalpractices among cctv control room operators in the uk. Surveillance and Society. 2004.[Smy97] P. Smyth. Clustering sequences with Hidden Markov Models. 1997.[SOM05] IAN SOMMERVILLE. INGENIERIA DEL SOFTWARE. ADDISON-WESLEY, 2005.[VG08] CRIM Valérie Gouaillier. Intelligent Video Surveillance: Promises and Challenges.2008.231
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UNIVERSIDAD DE CASTILLA-LA MANCHAES
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Roberto García BermejoE-mail: robe
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ResumenLos sitemas de vigilancia in
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3.2.4. Equivalencia de vectores. .
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B. Imágenes a color 223Bibliograf
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3.27. Evolución de la detección d
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A.6. Visualización de monitorizaci
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AgradecimientosMe gustaría agradec
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CAPÍTULO 1. INTRODUCCIÓNCada vez
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¿Qué es un sistema de vigilancia
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ProblemáticaCAPÍTULO 1. INTRODUCC
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Estructura del documentoCAPÍTULO 1
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CAPÍTULO 2. OBJETIVOSserán almace
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3Antecedentes.3.1. Introducción ge
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CAPÍTULO 3. ANTECEDENTES.Introducc
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CAPÍTULO 3. ANTECEDENTES.Fundament
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CAPÍTULO 3. ANTECEDENTES.Estado de
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CAPÍTULO 3. ANTECEDENTES.Necesidad
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CAPÍTULO 3. ANTECEDENTES.Tendencia
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CAPÍTULO 3. ANTECEDENTES.Análisis
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CAPÍTULO 3. ANTECEDENTES.Ejemplos
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Metodología de desarrolloCAPÍTULO
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Tecnologías y herramientas utiliza
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Tecnologías y herramientas utiliza
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5Arquitectura de AtravelEn este cap
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CAPÍTULO 5. ARQUITECTURA DE ATRAVE
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EvoluciónCAPÍTULO 6. EVOLUCIÓN Y
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Page 202 and 203: EvoluciónCAPÍTULO 6. EVOLUCIÓN Y
Page 204 and 205: ResultadosCAPÍTULO 6. EVOLUCIÓN Y
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Page 228 and 229: Conclusiones personalesCAPÍTULO 7.
Page 231 and 232: AManual de referenciaA.1.Manual de
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