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Multi-Camera Networks: Principles and Applications

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  • The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring 
  • Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications
  • Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware

This book is the definitive reference in multi-camera networks. It gives clear guidance on the conceptual and implementation issues involved in the design and operation of multi-camera networks, as well as presenting the state-of-the-art in hardware, algorithms and system development. The book is broad in scope, covering smart camera architectures, embedded processing, sensor fusion and middleware, calibration and topology, network-based detection and tracking, and applications in distributed and collaborative methods in camera networks. This book will be an ideal reference for university researchers, R&D engineers, computer engineers, and graduate students working in signal and video processing, computer vision, and sensor networks.

Hamid Aghajan is a Professor of Electrical Engineering (consulting) at Stanford University. His research is on multi-camera networks for smart environments with application to smart homes, assisted living and well being, meeting rooms, and avatar-based communication and social interactions. He is Editor-in-Chief of Journal of Ambient Intelligence and Smart Environments, and was general chair of ACM/IEEE ICDSC 2008.

Andrea Cavallaro is Reader (Associate Professor) at Queen Mary, University of London (QMUL). His research is on target tracking and audiovisual content analysis for advanced surveillance and multi-sensor systems. He serves as Associate Editor of the IEEE Signal Processing Magazine and the IEEE Trans. on Multimedia, and has been general chair of IEEE AVSS 2007, ACM/IEEE ICDSC 2009 and BMVC 2009.

  • The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring 
  • Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications
  • Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware

    • Author(s): Hamid Aghajan, Andrea Cavallaro
    Publisher: Academic Press
    Year: 2009

    Language: English
    Pages: 588

    Cover Page
    ......Page 1
    Copyright......Page 2
    Foreword......Page 3
    Distributed Processing in Multi-Camera Networks......Page 6
    Multi-Camera Calibration and Topology......Page 7
    Active and Heterogeneous Camera Networks......Page 8
    Multi-Camera Human Detection, Tracking, Pose, and Behavior Analysis......Page 9
    Smart Camera Networks: Architecture, Middleware, and Applications......Page 10
    Acknowledgments......Page 12
    Introduction......Page 13
    Perspective Projection......Page 14
    Camera Matrices......Page 15
    Estimating the Camera Matrix......Page 17
    Two-Camera Geometry......Page 18
    Epipolar Geometry and Its Estimation......Page 20
    Relating the Fundamental Matrix to the Camera Matrices......Page 21
    Estimating the Fundamental Matrix......Page 22
    Projective Transformations......Page 24
    Estimating Projective Transformations......Page 26
    Rectifying Projective Transformations......Page 27
    Feature Detection and Matching......Page 28
    Affine Reconstruction......Page 30
    Metric Reconstruction......Page 32
    Bundle Adjustment......Page 34
    Resources......Page 35
    References......Page 36
    Introduction......Page 38
    Camera Network Calibration and Synchronization......Page 40
    Related Work......Page 43
    Camera Network Calibration......Page 48
    Camera Network Synchronization......Page 51
    Results......Page 53
    Dynamic Scene Reconstruction from Silhouette Cues......Page 58
    Related Work......Page 59
    Probabilistic Framework......Page 61
    Automatic Learning and Tracking......Page 70
    Results and Evaluation......Page 73
    Conclusions......Page 80
    References......Page 81
    Introduction......Page 85
    Base Triangle......Page 88
    Large-Scale Networks......Page 89
    Bundle Adjustment Refinement......Page 91
    Actuation Strategies......Page 92
    System Description......Page 93
    Actuated Camera Platform......Page 94
    Network Architecture......Page 95
    Localization Accuracy......Page 96
    Node Density......Page 98
    Latency......Page 100
    References......Page 101
    Introduction......Page 103
    Simplicial Homology......Page 105
    Example......Page 106
    Čech Theorem......Page 107
    The Camera and the Environment Models......Page 108
    The CN-Complex......Page 109
    Recovering Topology: 2D Case......Page 111
    Algorithms......Page 112
    Simulation in 2D......Page 114
    Recovering Topology: 2.5D Case......Page 116
    Building the CN-Complex......Page 117
    Experimentation......Page 118
    References......Page 122
    Introduction......Page 124
    Related Work......Page 125
    Definitions......Page 127
    Modeling a Camera's Field of View......Page 128
    Modeling Space......Page 130
    Exact Algorithms......Page 131
    Heuristics......Page 135
    Random Selection and Placement......Page 137
    Experiments......Page 138
    Comparison of Approaches......Page 139
    Complex Space Examples......Page 141
    Possible Extensions......Page 143
    References......Page 144
    6 Optimal Visual Sensor Network Configuration......Page 146
    Introduction......Page 147
    Related Work......Page 148
    General Visibility Model......Page 149
    Visibility Model for Visual Tagging......Page 151
    Discretization of Camera and Tag Spaces......Page 154
    MIN_CAM: Minimizing the Number of Cameras for Target Visibility......Page 155
    FIX_CAM: Maximizing Visibility for a Given Number of Cameras......Page 156
    GREEDY: An Algorithm to Speed Up BIP......Page 158
    Optimal Camera Placement Simulation Experiments......Page 159
    Comparison with Other Camera Placement Strategies......Page 165
    Conclusions and Future Work......Page 167
    References......Page 168
    Introduction......Page 170
    Related Work......Page 171
    Tracking......Page 173
    Objective Function for PTZ Scheduling......Page 175
    Asynchronous Optimization......Page 176
    View Angle......Page 178
    Target--Zone Boundary Distance......Page 181
    Combined Quality Measure......Page 182
    Experiments......Page 183
    References......Page 191
    Introduction......Page 194
    Related Work......Page 195
    Pan-Tilt-Zoom Camera Geometry......Page 197
    PTZ Camera Networks with Master--Slave Configuration......Page 198
    Minimal PTZ Camera Model Parameterization......Page 199
    Cooperative Target Tracking......Page 200
    Tracking Using SIFT Visual Landmarks......Page 201
    Extension to Wider Areas......Page 203
    The Vanishing Line for Zoomed Head Localization......Page 205
    Experimental Results......Page 208
    Conclusions......Page 213
    References......Page 214
    Introduction......Page 217
    Architecture Design in Multi-Modal Systems......Page 218
    Logical Architecture Design......Page 219
    Physical Architecture Design......Page 221
    Data Alignment......Page 225
    Multi-Modal Techniques for State Estimation and Localization......Page 227
    Fusion of Multi-Modal Cues for Event Analysis......Page 233
    Applications......Page 234
    Ambient Intelligence Applications......Page 235
    Conclusions......Page 238
    References......Page 239
    Introduction......Page 242
    Cameras......Page 243
    Projective Geometry for Catadioptric Systems......Page 244
    Spherical Camera Model......Page 246
    Image Processing on the Sphere......Page 248
    Intrinsic Parameters......Page 250
    Extrinsic Parameters......Page 252
    Epipolar Geometry for Paracatadioptric Cameras......Page 253
    Disparity Estimation......Page 255
    Correlation Estimation with Sparse Approximations......Page 259
    Distributed Coding of 3D Scenes......Page 261
    Conclusions......Page 264
    References......Page 265
    Introduction......Page 268
    Classic Approach to Video Coding......Page 269
    Slepian-Wolf Theorem......Page 273
    A Simple Example......Page 275
    Channel Codes for Binary Source DSC......Page 276
    Wyner-Ziv Theorem......Page 278
    Applying DSC to Video Coding......Page 279
    PRISM Codec......Page 281
    Stanford Approach......Page 283
    Remarks......Page 286
    Applying DVC to Multi-View Systems......Page 289
    Extending Mono-View Codecs......Page 290
    Remarks on Multi-View Problems......Page 292
    References......Page 293
    Introduction......Page 296
    Foundations of Distributed Source Coding......Page 297
    Structure and Properties of the Plenoptic Data......Page 300
    Distributed Compression of Multi-View Images......Page 302
    Multi-Terminal Distributed Video Coding......Page 307
    Conclusions......Page 308
    References......Page 309
    Introduction......Page 311
    Co-Training......Page 314
    Boosting for Feature Selection......Page 315
    Co-Training System......Page 317
    Scene Calibration......Page 318
    Online Co-Training......Page 319
    Experimental Results......Page 322
    Indoor Scenario......Page 323
    Outdoor Scenario......Page 326
    Resources......Page 327
    References......Page 330
    Introduction......Page 333
    Background......Page 334
    Tracking......Page 335
    Example-Based Methods......Page 336
    Segmentation......Page 337
    Reconstruction......Page 339
    Linear Discriminant Analysis......Page 342
    Average Neighborhood Margin Maximization......Page 343
    3D Haarlets......Page 345
    Training......Page 346
    Experiments......Page 349
    Rotation Invariance......Page 350
    Overhead Tracker......Page 351
    Experiments......Page 354
    Results and Conclusions......Page 355
    References......Page 357
    Introduction......Page 360
    Key Factors and Related Work......Page 361
    Approach and Chapter Organization......Page 365
    Bayesian Tracking Problem Formulation......Page 366
    Single-Object 3D State and Model Representation......Page 367
    Joint Dynamic Model......Page 368
    Single-Object Dynamic Model......Page 370
    Color Likelihood......Page 372
    Reversible-Jump MCMC......Page 375
    Move Proposals......Page 376
    Calibration and Slant Removal......Page 379
    Results......Page 380
    Conclusions......Page 382
    References......Page 384
    Introduction......Page 386
    Background Modeling......Page 388
    Single-Camera Person Tracking......Page 390
    The Tracking Algorithm......Page 391
    Occlusion Detection and Classification......Page 395
    Bayesian-Competitive Consistent Labeling......Page 397
    Trajectory Shape Analysis for Abnormal Path Detection......Page 401
    Trajectory Shape Classification......Page 404
    Experimental Results......Page 406
    References......Page 409
    Introduction......Page 411
    Related Work......Page 413
    Multiple Stationary Cameras with Overlapping Fields of View......Page 414
    Multiple Pan-Tilt-Zoom Cameras......Page 415
    Evaluating an Association Using Appearance Information......Page 416
    Estimating the Subspace of BTFs Between Cameras......Page 417
    Data Model......Page 418
    Maximum Likelihood Estimation......Page 420
    Simulations......Page 422
    Real Sequences......Page 424
    Conclusions......Page 426
    References......Page 427
    Introduction......Page 430
    Single-Camera Surveillance System Architecture......Page 434
    System Design......Page 435
    Cross-Camera Calibration......Page 437
    Data Fusion......Page 441
    Critical Infrastructure Protection......Page 444
    Hazardous Lab Safety Verification......Page 446
    Testing and Results......Page 447
    Future Work......Page 448
    References......Page 449
    19 Composite Event Detection in Multi-Camera and Multi-Sensor Surveillance Networks......Page 452
    Introduction......Page 453
    Related Work......Page 454
    System Infrastructure......Page 456
    Event Representation and Detection......Page 458
    Primitive Events and User Interfaces......Page 460
    Composite Event Search......Page 463
    Query-Based Search and Browsing......Page 464
    Application: Retail Loss Prevention......Page 467
    Application: Tailgating Detection......Page 469
    Application: False Positive Reduction......Page 471
    References......Page 472
    Introduction......Page 476
    The Evolution of Smart Camera Systems......Page 478
    Single Smart Cameras......Page 479
    Distributed Smart Cameras......Page 480
    Smart Cameras in Sensor Networks......Page 481
    Future and Challenges......Page 483
    Distributed Algorithms......Page 484
    Privacy and Security......Page 485
    Conclusions......Page 486
    References......Page 487
    Introduction......Page 490
    Centralized Processing......Page 491
    Distributed Processing......Page 493
    Sensor Modules......Page 494
    Processing Module......Page 495
    Communication Modules......Page 497
    MeshEye......Page 498
    CMUcam3......Page 499
    Conclusions......Page 500
    References......Page 501
    Introduction......Page 504
    Smart Cameras......Page 505
    Distributed Smart Cameras......Page 506
    Challenges of Distributed Smart Cameras......Page 507
    Middleware Architecture......Page 508
    Middleware for Embedded Systems......Page 510
    Specific Requirements of Distributed Smart Cameras......Page 511
    From Objects to Agents......Page 512
    Code Mobility and Programming Languages......Page 513
    Mobile Agents for Embedded Smart Cameras......Page 514
    DSCAgents......Page 515
    Decentralized Multi-Camera Tracking......Page 519
    Sensor Fusion......Page 523
    Conclusions......Page 526
    References......Page 527
    Introduction......Page 531
    Event-Driven Clustering Protocols......Page 533
    Distributed Kalman Filtering......Page 536
    Object Tracking with Wireless Camera Networks......Page 538
    Clustering Protocol......Page 540
    Kalman Filter Equations......Page 546
    State Estimation......Page 550
    Experimental Results......Page 552
    Simulator Environment......Page 553
    Testbed Implementation......Page 558
    Conclusions and Future Work......Page 560
    References......Page 561
    Systems and Algorithms in Realistic Scenarios......Page 565
    Interfacing Vision Processing and Reasoning......Page 566
    Performance Evaluation......Page 568
    User and Social Acceptance......Page 569
    Conclusions......Page 570
    A......Page 571
    C......Page 572
    D......Page 575
    E......Page 576
    F......Page 577
    I......Page 578
    M......Page 579
    O......Page 581
    P......Page 582
    R......Page 583
    S......Page 584
    T......Page 586
    W......Page 587
    Z......Page 588