Includes bibliographical references and index.

Online resource; title from PDF title page (SpringerLink, viewed March 28, 2017).

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 domain: safety, efficiency, and law enforcement. The individual chapters present significant applications within those problem domains, each presented in a tutorial manner, describing the motivation for and benefits of the application, and a description of the state of the art. Key features: -Surveys the applications of computer vision techniques to road transportation system for the purposes of improving safety and efficiency and to assist law enforcement. -Offers a timely discussion as computer vision is reaching a point of being useful in the field of transportation systems. -Available as an enhanced eBook with video demonstrations to further explain the concepts discussed in the book, as well as links to publically available software and data sets for testing and algorithm development. The book will benefit the many researchers, engineers and practitioners of computer vision, digital imaging, automotive and civil engineering working in intelligent transportation systems. Given the breadth of topics covered, the text will present the reader with new and yet unconceived possibilities for application within their communities.

-- 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.