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Computer vision and imaging in intelligent transportation systems /
Acts as single source reference providing readers with an overview of how computer vision can contribute to the different applications in the field of road transportation This book presents a survey of computer vision techniques related to three key broad problems in the roadway transportation domai...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ : London, UK :
Wiley ; IEEE Press,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- List of Contributors xiii
- Preface xvii
- Acknowledgments xxi
- About the Companion Website xxiii
- 1 Introduction 1
- Raja Bala and Robert P. Loce
- 1.1 Law Enforcement and Security 1
- 1.2 Efficiency 4
- 1.3 Driver Safety and Comfort 5
- 1.4 A Computer Vision Framework for Transportation Applications 7
- 1.4.1 Image and Video Capture 8
- 1.4.2 Data Preprocessing 8
- 1.4.3 Feature Extraction 9
- 1.4.4 Inference Engine 10
- 1.4.5 Data Presentation and Feedback 11
- Part I Imaging from the Roadway Infrastructure 15
- 2 Automated License Plate Recognition 17
- Aaron Burry and Vladimir Kozitsky
- 2.1 Introduction 17
- 2.2 Core ALPR Technologies 18
- 2.2.1 License Plate Localization 19
- 2.2.2 Character Segmentation 24
- 2.2.3 Character Recognition 28
- 2.2.4 State Identification 38
- 3 Vehicle Classification 47
- Shashank Deshpande, Wiktor Muron and Yang Cai
- 3.1 Introduction 47
- 3.2 Overview of the Algorithms 48
- 3.3 Existing AVC Methods 48
- 3.4 LiDAR Imaging-Based 49
- 3.4.1 LiDAR Sensors 49
- 3.4.2 Fusion of LiDAR and Vision Sensors 50
- 3.5 Thermal Imaging?-Based 53
- 3.5.1 Thermal Signatures 53
- 3.5.2 Intensity Shape?-Based 56
- 3.6 Shape?- and Profile?-Based 58
- 3.6.1 Silhouette Measurements 60
- 3.6.2 Edge?-Based Classification 65
- 3.6.3 Histogram of Oriented Gradients 67
- 3.6.4 Haar Features 68
- 3.6.5 Principal Component Analysis 69
- 3.7 Intrinsic Proportion Model 72
- 3.8 3D Model?-Based Classification 74
- 3.9 SIFT?-Based Classification 74
- 3.10 Summary 75
- 4 Detection of Passenger Compartment Violations 81
- Orhan Bulan, Beilei Xu, Robert P. Loce and Peter Paul
- 4.1 Introduction 81
- 4.2 Sensing within the Passenger Compartment 82
- 4.2.1 Seat Belt Usage Detection 82
- 4.2.2 Cell Phone Usage Detection 83
- 4.2.3 Occupancy Detection 83
- 4.3 Roadside Imaging 84
- 4.3.1 Image Acquisition Setup 84
- 4.3.2 Image Classification Methods 85
- 4.3.3 Detection?-Based Methods 94
- 5 Detection of Moving Violations 101.
- Wencheng Wu, Orhan Bulan, Edgar A. Bernal and Robert P. Loce
- 5.1 Introduction 101
- 5.2 Detection of Speed Violations 101
- 5.2.1 Speed Estimation from Monocular Cameras 102
- 5.2.2 Speed Estimation from Stereo Cameras 108
- 5.2.3 Discussion 115
- 5.3 Stop Violations 115
- 5.3.1 Red Light Cameras 115
- 5.4 Other Violations 125
- 5.4.1 Wrong?-Way Driver Detection 125
- 5.4.2 Crossing Solid Lines 126
- 6 Traffic Flow Analysis 131
- Rodrigo Fernandez, Muhammad Haroon Yousaf, Timothy J. Ellis, Zezhi Chen and Sergio A. Velastin
- 6.1 What is Traffic Flow Analysis? 131
- 6.1.1 Traffic Conflicts and Traffic Analysis 131
- 6.1.2 Time Observation 132
- 6.1.3 Space Observation 133
- 6.1.4 The Fundamental Equation 133
- 6.1.5 The Fundamental Diagram 133
- 6.1.6 Measuring Traffic Variables 134
- 6.1.7 Road Counts 135
- 6.1.8 Junction Counts 135
- 6.1.9 Passenger Counts 136
- 6.1.10 Pedestrian Counts 136
- 6.1.11 Speed Measurement 136
- 6.2 The Use of Video Analysis in Intelligent Transportation Systems 137
- 6.2.1 Introduction 137
- 6.2.2 General Framework for Traffic Flow Analysis 137
- 6.2.3 Application Domains 143
- 6.3 Measuring Traffic Flow from Roadside CCTV Video 144
- 6.3.1 Video Analysis Framework 144
- 6.3.2 Vehicle Detection 146
- 6.3.3 Background Model 146
- 6.3.4 Counting Vehicles 149
- 6.3.5 Tracking 150
- 6.3.6 Camera Calibration 150
- 6.3.7 Feature Extraction and Vehicle Classification 152
- 6.3.8 Lane Detection 153
- 6.3.9 Results 155
- 6.4 Some Challenges 156
- 7 Intersection Monitoring Using Computer Vision Techniques for Capacity, Delay, and Safety Analysis 163
- Brendan Tran Morris and Mohammad Shokrolah Shirazi
- 7.1 Vision?-Based Intersection Analysis: Capacity, Delay, and Safety 163
- 7.1.1 Intersection Monitoring 163
- 7.1.2 Computer Vision Application 164
- 7.2 System Overview 165
- 7.2.1 Tracking Road Users 166
- 7.2.2 Camera Calibration 169
- 7.3 Count Analysis 171
- 7.3.1 Vehicular Counts 171
- 7.3.2 Nonvehicular Counts 173.
- 7.4 Queue Length Estimation 173
- 7.4.1 Detection?-Based Methods 174
- 7.4.2 Tracking?-Based Methods 175
- 7.5 Safety Analysis 177
- 7.5.1 Behaviors 178
- 7.5.2 Accidents 182
- 7.5.3 Conflicts 185
- 7.6 Challenging Problems and Perspectives 187
- 7.6.1 Robust Detection and Tracking 187
- 7.6.2 Validity of Prediction Models for Conflict and Collisions 188
- 7.6.3 Cooperating Sensing Modalities 189
- 7.6.4 Networked Traffic Monitoring Systems 189
- 7.7 Conclusion 189
- 8 Video?-Based Parking Management 195
- Oliver Sidla and Yuriy Lipetski
- 8.1 Introduction 195
- 8.2 Overview of Parking Sensors 197
- 8.3 Introduction to Vehicle Occupancy Detection Methods 200
- 8.4 Monocular Vehicle Detection 200
- 8.4.1 Advantages of Simple 2D Vehicle Detection 200
- 8.4.2 Background Model-Based Approaches 200
- 8.4.3 Vehicle Detection Using Local Feature Descriptors 202
- 8.4.4 Appearance?-Based Vehicle Detection 203
- 8.4.5 Histograms of Oriented Gradients 204
- 8.4.6 LBP Features and LBP Histograms 207
- 8.4.7 Combining Detectors into Cascades and Complex Descriptors 208
- 8.4.8 Case Study: Parking Space Monitoring Using a Combined Feature Detector 208
- 8.4.9 Detection Using Artificial Neural Networks 211
- 8.5 Introduction to Vehicle Detection with 3D Methods 213
- 8.6 Stereo Vision Methods 215
- 8.6.1 Introduction to Stereo Methods 215
- 8.6.2 Limits on the Accuracy of Stereo Reconstruction 216
- 8.6.3 Computing the Stereo Correspondence 217
- 8.6.4 Simple Stereo for Volume Occupation Measurement 218
- 8.6.5 A Practical System for Parking Space Monitoring Using a Stereo System 218
- 8.6.6 Detection Methods Using Sparse 3D Reconstruction 220
- 9 Video Anomaly Detection 227
- Raja Bala and Vishal Monga
- 9.1 Introduction 227
- 9.2 Event Encoding 228
- 9.2.1 Trajectory Descriptors 229
- 9.2.2 Spatiotemporal Descriptors 231
- 9.3 Anomaly Detection Models 233
- 9.3.1 Classification Methods 233
- 9.3.2 Hidden Markov Models 234
- 9.3.3 Contextual Methods 234.
- 9.4 Sparse Representation Methods for Robust Video Anomaly Detection 236
- 9.4.1 Structured Anomaly Detection 237
- 9.4.2 Unstructured Video Anomaly Detection 243
- 9.4.3 Experimental Setup and Results 245
- 9.5 Conclusion and Future Research 253
- Part II Imaging from and within the Vehicle 257
- 10 Pedestrian Detection 259
- Shashank Deshpande and Yang Cai
- 10.1 Introduction 259
- 10.2 Overview of the Algorithms 259
- 10.3 Thermal Imaging 260
- 10.4 Background Subtraction Methods 261
- 10.4.1 Frame Subtraction 261
- 10.4.2 Approximate Median 262
- 10.4.3 Gaussian Mixture Model 263
- 10.5 Polar Coordinate Profile 263
- 10.6 Image?-Based Features 265
- 10.6.1 Histogram of Oriented Gradients 265
- 10.6.2 Deformable Parts Model 266
- 10.6.3 LiDAR and Camera Fusion-Based Detection 266
- 10.7 LiDAR Features 268
- 10.7.1 Preprocessing Module 268
- 10.7.2 Feature Extraction Module 268
- 10.7.3 Fusion Module 268
- 10.7.4 LIPD Dataset 270
- 10.7.5 Overview of the Algorithm 270
- 10.7.6 LiDAR Module 272
- 10.7.7 Vision Module 275
- 10.7.8 Results and Discussion 276
- 10.7.8.1 LiDAR Module 276
- 10.7.8.2 Vision Module 276
- 10.8 Summary 280
- 11 Lane Detection and Tracking Problems in Lane Departure Warning Systems 283
- Gianni Cario, Alessandro Casavola and Marco Lupia
- 11.1 Introduction 283
- 11.2 LD: Algorithms for a Single Frame 285
- 11.2.1 Image Preprocessing 285
- 11.2.2 Edge Extraction 287
- 11.2.3 Stripe Identification 291
- 11.2.4 Line Fitting 294
- 11.3 LT Algorithms 297
- 11.3.1 Recursive Filters on Subsequent N frames 298
- 11.3.2 Kalman Filter 298
- 11.4 Implementation of an LD and LT Algorithm 299
- 11.4.1 Simulations 300
- 11.4.2 Test Driving Scenario 300
- 11.4.3 Driving Scenario: Lane Departures at Increasing Longitudinal Speed 300
- 11.4.4 The Proposed Algorithm 302
- 11.4.5 Conclusions 303
- 12 Vision?-Based Integrated Techniques for Collision Avoidance Systems 305
- Ravi Satzoda and Mohan Trivedi
- 12.1 Introduction 305.
- 12.2 Related Work 307
- 12.3 Context Definition for Integrated Approach 307
- 12.4 ELVIS: Proposed Integrated Approach 308
- 12.4.1 Vehicle Detection Using Lane Information 309
- 12.4.2 Improving Lane Detection using On?-Road Vehicle Information 312
- 12.5 Performance Evaluation 313
- 12.5.1 Vehicle Detection in ELVIS 313
- 12.5.2 Lane Detection in ELVIS 316
- 12.6 Concluding Remarks 319
- 13 Driver Monitoring 321
- Raja Bala and Edgar A. Bernal
- 13.1 Introduction 321
- 13.2 Video Acquisition 322
- 13.3 Face Detection and Alignment 323
- 13.4 Eye Detection and Analysis 325
- 13.5 Head Pose and Gaze Estimation 326
- 13.5.1 Head Pose Estimation 326
- 13.5.2 Gaze Estimation 328
- 13.6 Facial Expression Analysis 332
- 13.7 Multimodal Sensing and Fusion 334
- 13.8 Conclusions and Future Directions 336
- 14 Traffic Sign Detection and Recognition 343
- Hasan Fleyeh
- 14.1 Introduction 343
- 14.2 Traffic Signs 344
- 14.2.1 The European Road and Traffic Signs 344
- 14.2.2 The American Road and Traffic Signs 347
- 14.3 Traffic Sign Recognition 347
- 14.4 Traffic Sign Recognition Applications 348
- 14.5 Potential Challenges 349
- 14.6 Traffic Sign Recognition System Design 349
- 14.6.1 Traffic Signs Datasets 352
- 14.6.2 Colour Segmentation 354
- 14.6.3 Traffic Sign's Rim Analysis 359
- 14.6.4 Pictogram Extraction 364
- 14.6.5 Pictogram Classification Using Features 365
- 14.7 Working Systems 369
- 15 Road Condition Monitoring 375
- Matti Kutila, Pasi Pyykonen, Johan Casselgren and Patrik Jonsson
- 15.1 Introduction 375
- 15.2 Measurement Principles 376
- 15.3 Sensor Solutions 377
- 15.3.1 Camera?-Based Friction Estimation Systems 377
- 15.3.2 Pavement Sensors 379
- 15.3.3 Spectroscopy 380
- 15.3.4 Roadside Fog Sensing 382
- 15.3.5 In?-Vehicle Sensors 383
- 15.4 Classification and Sensor Fusion 386
- 15.5 Field Studies 390
- 15.6 Cooperative Road Weather Services 394
- 15.7 Discussion and Future Work 395
- Index 399.