Intelligent Video Surveillance Systems

Intelligent Video Surveillance Systems

By: Jean-Yves Dufour (editor)Hardback

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Description

Belonging to the wider academic field of computer vision, video analytics has aroused a phenomenal surge of interest since the current millennium. Video analytics is intended to solve the problem of the incapability of exploiting video streams in real time for the purpose of detection or anticipation. It involves analyzing the videos using algorithms that detect and track objects of interest over time and that indicate the presence of events or suspect behavior involving these objects. The aims of this book are to highlight the operational attempts of video analytics, to identify possible driving forces behind potential evolutions in years to come, and above all to present the state of the art and the technological hurdles which have yet to be overcome. The need for video surveillance is introduced through two major applications (the security of rail transportation systems and a posteriori investigation). The characteristics of the videos considered are presented through the cameras which enable capture and the compression methods which allow us to transport and store them. Technical topics are then discussed the analysis of objects of interest (detection, tracking and recognition), high-level video analysis, which aims to give a semantic interpretation of the observed scene (events, behaviors, types of content). The book concludes with the problem of performance evaluation.

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Contents

Introduction xiii Jean-Yves DUFOUR and Phlippe MOUTTOU Chapter 1. Image Processing: Overview and Perspectives 1 Henri MAITRE 1.1. Half a century ago 1 1.2. The use of images 3 1.3. Strengths and weaknesses of image processing 4 1.3.1. What are these theoretical problems that image processing has been unable to overcome? 5 1.3.2. What are the problems that image processing has overcome? 5 1.4. What is left for the future? 6 1.5. Bibliography 9 Chapter 2. Focus on Railway Transport 13 Sebastien AMBELLOUIS and Jean-Luc BRUYELLE 2.1. Introduction. 13 2.2. Surveillance of railway infrastructures 15 2.2.1. Needs analysis 15 2.2.2. Which architectures? 16 2.2.3. Detection and analysis of complex events 17 2.2.4. Surveillance of outside infrastructures 20 2.3. Onboard surveillance 21 2.3.1. Surveillance of buses 22 2.3.2. Applications to railway transport 23 2.4. Conclusion 28 2.5. Bibliography 30 Chapter 3. A Posteriori Analysis for Investigative Purposes 33 Denis MARRAUD, Benjamin CEPAS, Jean-Francois SULZER, Christianne MULAT and Florence SEDES 3.1. Introduction 33 3.2. Requirements in tools for assisted investigation 34 3.2.1. Prevention and security 34 3.2.2. Information gathering 35 3.2.3. Inquiry 36 3.3. Collection and storage of data 36 3.3.1. Requirements in terms of standardization 37 3.3.2. Attempts at standardization (AFNOR and ISO) 37 3.4. Exploitation of the data 39 3.4.1. Content-based indexing 39 3.4.2. Assisted investigation tools 43 3.5. Conclusion 44 3.6. Bibliography 45 Chapter 4. Video Surveillance Cameras 47 Cedric LE BARZ and Thierry LAMARQUE 4.1. Introduction 47 4.2. Constraints 48 4.2.1. Financial constraints 48 4.2.2. Environmental constraints 49 4.3. Nature of the information captured 49 4.3.1. Spectral bands 50 4.3.2. 3D or 2D + Z imaging 51 4.4. Video formats 53 4.5. Technologies 55 4.6. Interfaces: from analog to IP 57 4.6.1. From analog to digital 57 4.6.2. The advent of IP 59 4.6.3. Standards. 60 4.7. Smart cameras 61 4.8. Conclusion 62 4.9. Bibliography 63 Chapter 5. Video Compression Formats 65 Marc LENY and Didier NICHOLSON 5.1. Introduction 65 5.2. Video formats 66 5.2.1. Analog video signals 66 5.2.2. Digital video: standard definition 67 5.2.3. High definition 68 5.2.4. The CIF group of formats 69 5.3. Principles of video compression 70 5.3.1. Spatial redundancy 70 5.3.2. Temporal redundancy 73 5.4. Compression standards 74 5.4.1. MPEG-2 74 5.4.2. MPEG-4 Part 2 75 5.4.3. MPEG-4 Part 10/H.264 AVC 77 5.4.4. MPEG-4 Part 10/H.264 SVC 79 5.4.5. Motion JPEG 2000 80 5.4.6. Summary of the formats used in video surveillance 82 5.5. Conclusion 83 5.6. Bibliography 84 Chapter 6. Compressed Domain Analysis for Fast Activity Detection 87 Marc LENY 6.1. Introduction 87 6.2. Processing methods 88 6.2.1. Use of transformed coefficients in the frequency domain 88 6.2.2. Use of motion estimation 90 6.2.3. Hybrid approaches 91 6.3. Uses of analysis of the compressed domain 93 6.3.1. General architecture 94 6.3.2. Functions for which compressed domain analysis is reliable 96 6.3.3. Limitations. 97 6.4. Conclusion 100 6.5. Acronyms 101 6.6. Bibliography 101 Chapter 7. Detection of Objects of Interest 103 Yoann DHOME, Bertrand LUVISON, Thierry CHESNAIS, Rachid BELAROUSSI, Laurent LUCAT, Mohamed CHAOUCH and Patrick SAYD 7.1. Introduction. 103 7.2. Moving object detection 104 7.2.1. Object detection using background modeling 104 7.2.2. Motion-based detection of objects of interest 107 7.3. Detection by modeling of the objects of interest 109 7.3.1. Detection by geometric modeling 109 7.3.2. Detection by visual modeling. 111 7.4. Conclusion 117 7.5. Bibliography 118 Chapter 8. Tracking of Objects of Interest in a Sequence of Images 123 Simona MAGGIO, Jean-Emmanuel HAUGEARD, Boris MEDEN, Bertrand LUVISON, Romaric AUDIGIER, Brice BURGER and Quoc Cuong PHAM 8.1. Introduction 123 8.2. Representation of objects of interest and their associated visual features 124 8.2.1. Geometry 124 8.2.2. Characteristics of appearance 125 8.3. Geometric workspaces 127 8.4. Object-tracking algorithms 127 8.4.1. Deterministic approaches 127 8.4.2. Probabilistic approaches 128 8.5. Updating of the appearance models 132 8.6. Multi-target tracking 135 8.6.1. MHT and JPDAF 135 8.6.2. MCMC and RJMCMC sampling techniques 136 8.6.3. Interactive filters, track graph 138 8.7. Object tracking using a PTZ camera 138 8.7.1. Object tracking using a single PTZ camera only 139 8.7.2. Object tracking using a PTZ camera coupled with a static camera 139 8.8. Conclusion 141 8.9. Bibliography 142 Chapter 9. Tracking Objects of Interest Through a Camera Network 147 Catherine ACHARD, Sebastien AMBELLOUIS, Boris MEDEN,Sebastien LEFEBVRE and Dung Nghi TRUONG CONG 9.1. Introduction 147 9.2. Tracking in a network of cameras whose fields of view overlap 148 9.2.1. Introduction and applications 148 9.2.2. Calibration and synchronization of a camera network 150 9.2.3. Description of the scene by multi-camera aggregation 153 9.3. Tracking through a network of cameras with non-overlapping fields of view 155 9.3.1. Issues and applications 155 9.3.2. Geometric and/or photometric calibration of a camera network 156 9.3.3. Reidentification of objects of interest in a camera network 157 9.3.4. Activity recognition/event detection in a camera network 160 9.4. Conclusion 161 9.5. Bibliography 161 Chapter 10. Biometric Techniques Applied to Video Surveillance 165 Bernadette DORIZZI and Samuel VINSON 10.1. Introduction 165 10.2. The databases used for evaluation166 10.2.1. NIST-Multiple Biometrics Grand Challenge (NIST-MBGC) 167 10.2.2. Databases of faces 167 10.3. Facial recognition 168 10.3.1. Face detection 168 10.3.2. Face recognition in biometrics 169 10.3.3. Application to video surveillance 170 10.4. Iris recognition 173 10.4.1. Methods developed for biometrics 173 10.4.2. Application to video surveillance 174 10.4.3. Systems for iris capture in videos 176 10.4.4. Summary and perspectives 177 10.5. Research projects 177 10.6. Conclusion 178 10.7. Bibliography 179 Chapter 11. Vehicle Recognition in Video Surveillance 183 Stephane HERBIN 11.1. Introduction 183 11.2. Specificity of the context 184 11.2.1. Particular objects 184 11.2.2. Complex integrated chains 185 11.3. Vehicle modeling 185 11.3.1. Wire models 186 11.3.2. Global textured models 187 11.3.3. Structured models 188 11.4. Exploitation of object models 189 11.4.1. A conventional sequential chain with limited performance 189 11.4.2. Improving shape extraction 190 11.4.3. Inferring 3D information. 191 11.4.4. Recognition without form extraction 192 11.4.5. Toward a finer description of vehicles 193 11.5. Increasing observability 194 11.5.1. Moving observer 194 11.5.2. Multiple observers 195 11.6. Performances 196 11.7. Conclusion 196 11.8. Bibliography 197 Chapter 12. Activity Recognition 201 Bernard BOULAY and Francois BREMOND 12.1. Introduction 201 12.2. State of the art 202 12.2.1. Levels of abstraction 202 12.2.2. Modeling and recognition of activities 203 12.2.3. Overview of the state of the art 206 12.3. Ontology 206 12.3.1. Objects of interest 207 12.3.2. Scenario models 208 12.3.3. Operators 209 12.3.4. Summary 210 12.4. Suggested approach: the ScReK system 210 12.5. Illustrations 212 12.5.1. Application at an airport 213 12.5.2. Modeling the behavior of elderly people 213 12.6. Conclusion 215 12.7. Bibliography 215 Chapter 13. Unsupervised Methods for Activity Analysis and Detection of Abnormal Events 219 Remi EMONET and Jean-Marc ODOBEZ 13.1. Introduction 219 13.2. An example of a topic model: PLSA 221 13.2.1. Introduction 221 13.2.2. The PLSA model 221 13.2.3. PLSA applied to videos 223 13.3. PLSM and temporal models 226 13.3.1. PLSM model 226 13.3.2. Motifs extracted by PLSM 228 13.4. Applications: counting, anomaly detection 230 13.4.1. Counting 230 13.4.2. Anomaly detection 230 13.4.3. Sensor selection 231 13.4.4. Prediction and statistics 233 13.5. Conclusion 233 13.6. Bibliography 233 Chapter 14. Data Mining in a Video Database 235 Luis PATINO, Hamid BENHADDA and Francois BREMOND 14.1. Introduction 235 14.2. State of the art 236 Table of Contents xi 14.3. Pre-processing of the data 237 14.4. Activity analysis and automatic classification 238 14.4.1. Unsupervised learning of zones of activity 239 14.4.2. Definition of behaviors 242 14.4.3. Relational analysis 243 14.5. Results and evaluations 245 14.6. Conclusion 248 14.7. Bibliography 249 Chapter 15. Analysis of Crowded Scenes in Video 251 Mikel RODRIGUEZ, Josef SIVIC and Ivan LAPTEV 15.1. Introduction 251 15.2. Literature review 253 15.2.1. Crowd motion modeling and segmentation 253 15.2.2. Estimating density of people in a crowded scene 254 15.2.3. Crowd event modeling and recognition 255 15.2.4. Detecting and tracking in a crowded scene 256 15.3. Data-driven crowd analysis in videos 257 15.3.1. Off-line analysis of crowd video database 258 15.3.2. Matching 258 15.3.3. Transferring learned crowd behaviors 260 15.3.4. Experiments and results 260 15.4. Density-aware person detection and tracking in crowds 262 15.4.1. Crowd model 263 15.4.2. Tracking detections 264 15.4.3. Evaluation 265 15.5. Conclusions and directions for future research 268 15.6. Acknowledgments 268 15.7. Bibliography 269 Chapter 16. Detection of Visual Context 273 Herve LE BORGNE and Aymen SHABOU 16.1. Introduction 273 16.2. State of the art of visual context detection 275 16.2.1. Overview 275 16.2.2. Visual description 276 16.2.3. Multiclass learning 278 16.3. Fast shared boosting 279 16.4. Experiments. 281 16.4.1. Detection of boats in the Panama Canal 281 16.4.2. Detection of the visual context in video surveillance 283 16.5. Conclusion 285 16.6. Bibliography 286 Chapter 17. Example of an Operational Evaluation Platform: PPSL 289 Stephane BRAUDEL 17.1. Introduction 289 17.2. Use of video surveillance: approach and findings 290 17.3. Current use contexts and new operational concepts 292 17.4. Requirements in smart video processing 293 17.5. Conclusion 294 Chapter 18. Qualification and Evaluation of Performances 297 Bernard BOULAY, Jean-Francois GOUDOU and Francois BREMOND 18.1. Introduction 297 18.2. State of the art 298 18.2.1. Applications 298 18.2.2. Process 299 18.3. An evaluation program: ETISEO 303 18.3.1. Methodology 303 18.3.2. Metrics 305 18.3.3. Summary 307 18.4. Toward a more generic evaluation 309 18.4.1. Contrast 310 18.4.2. Shadows 312 18.5. The Quasper project 312 18.6. Conclusion 313 18.7. Bibliography 314 List of Authors 315 Index 321

Product Details

  • publication date: 13/11/2012
  • ISBN13: 9781848214330
  • Format: Hardback
  • Number Of Pages: 340
  • ID: 9781848214330
  • weight: 664
  • ISBN10: 1848214332

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