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E-grāmata: Computer Vision and Imaging in Intelligent Transportation Systems

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  • Sērija : IEEE Press
  • Izdošanas datums: 20-Mar-2017
  • Izdevniecība: Wiley-IEEE Press
  • Valoda: eng
  • ISBN-13: 9781118971642
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Computer Vision and Imaging in Intelligent Transportation SystemsPalielināt
  • Formāts: PDF+DRM
  • Sērija : IEEE Press
  • Izdošanas datums: 20-Mar-2017
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  • Valoda: eng
  • ISBN-13: 9781118971642
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Computer Vision and Imaging in Intelligent Transportation Systems  

Robert P. Loce, PARC, A Xerox Company, USA

Raja Bala, Samsung Research America, USA

Mohan Trivedi, University of California, USA

 

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(14)
Raja Bala
Robert P. Lace
1.1 Law Enforcement and Security
1(3)
1.2 Efficiency
4(1)
1.3 Driver Safety and Comfort
5(2)
1.4 A Computer Vision Framework for Transportation Applications
7(5)
1.4.1 Image and Video Capture
8(1)
1.4.2 Data Preprocessing
8(1)
1.4.3 Feature Extraction
9(1)
1.4.4 Inference Engine
10(1)
1.4.5 Data Presentation and Feedback
11(1)
References
12(3)
Part I Imaging from the Roadway Infrastructure 15(242)
2 Automated License Plate Recognition
17(30)
Aaron Burry
Vladimir Kozitsky
2.1 Introduction
17(1)
2.2 Core ALPR Technologies
18(24)
2.2.1 License Plate Localization
19(1)
2.2.1.1 Color-Based Methods
20(1)
2.2.1.2 Edge-Based Methods
20(1)
2.2.1.3 Machine Learning-Based Approaches
23(1)
2.2.2 Character Segmentation
24(1)
2.2.2.1 Preprocessing for Rotation, Crop, and Shear
25(1)
2.2.2.2 Character-Level Segmentation
28(1)
2.2.3 Character Recognition
28(1)
2.2.3.1 Character Harvesting and Sorting
30(1)
2.2.3.2 Data Augmentation
31(1)
2.2.3.3 Feature Extraction
32(1)
2.2.3.4 Classifiers and Training
34(1)
2.2.3.5 Classifier Evaluation
37(1)
2.2.4 State Identification
38(4)
References
42(5)
3 Vehicle Classification
47(34)
Shashank Deshpande
Wiktor Muron
Yang Cal
3.1 Introduction
47(1)
3.2 Overview of the Algorithms
48(1)
3.3 Existing AVC Methods
48(1)
3.4 LiDAR Imaging-Based
49(4)
3.4.1 LiDAR Sensors
49(1)
3.4.2 Fusion of LiDAR and Vision Sensors
50(3)
3.5 Thermal Imaging-Based
53(5)
3.5.1 Thermal Signatures
53(3)
3.5.2 Intensity Shape-Based
56(2)
3.6 Shape- and Profile-Based
58(14)
3.6.1 Silhouette Measurements
60(5)
3.6.2 Edge-Based Classification
65(2)
3.6.3 Histogram of Oriented Gradients
67(1)
3.6.4 Haar Features
68(1)
3.6.5 Principal Component Analysis
69(3)
3.7 Intrinsic Proportion Model
72(2)
3.8 3D Model-Based Classification
74(1)
3.9 SIFT-Based Classification
74(1)
3.10 Summary
75(1)
References
75(6)
4 Detection of Passenger Compartment Violations
81(20)
Orhan Bulan
Beilei Xu
Robert P. Lace
Peter Paul
4.1 Introduction
81(1)
4.2 Sensing within the Passenger Compartment
82(2)
4.2.1 Seat Belt Usage Detection
82(1)
4.2.2 Cell Phone Usage Detection
83(1)
4.2.3 Occupancy Detection
83(1)
4.3 Roadside Imaging
84(12)
4.3.1 Image Acquisition Setup
84(1)
4.3.2 Image Classification Methods
85(1)
4.3.2.1 Windshield and Side Window Detection from HOV/HOT Images
86(1)
4.3.2.2 Image Classification for Violation Detection
90(4)
4.3.3 Detection-Based Methods
94(1)
4.3.3.1 Multiband Approaches for Occupancy Detection
94(1)
4.1.3.2 Single Band Approaches
95(1)
References
96(5)
5 Detection of Moving Violations
101(30)
Wencheng Wu
Orhan Bulan
Edgar A. Bernal
Robert P. Loce
5.1 Introduction
101(1)
5.2 Detection of Speed Violations
101(14)
5.2.1 Speed Estimation from Monocular Cameras
102(6)
5.2.2 Speed Estimation from Stereo Cameras
108(1)
5.2.2.1 Depth Estimation in Binocular Camera Systems
109(1)
5.2.2.2 Vehicle Detection from Sequences of Depth Maps
110(1)
5.2.2.3 Vehicle Tracking from Sequences of Depth Maps
113(1)
5.2.2.4 Speed Estimation from Tracking Data
114(1)
5.2.3 Discussion
115(1)
5.3 Stop Violations
115(10)
5.3.1 Red Light Cameras
115(1)
5.3.1.1 RLCs, Evidentiary Systems
116(1)
5.3.1.2 RLCs, Computer Vision Systems
118(5)
5.3.2 Stop Sign Enforcement Systems
123(2)
5.4 Other Violations
125(1)
5.4.1 Wrong-Way Driver Detection
125(1)
5.4.2 Crossing Solid Lines
126(1)
References
126(5)
6 Traffic Flow Analysis
131(32)
Rodrigo Fernandez
Muhammad Haroon Yousaf
Timothy J. Ellis
Zezhi Chen
Sergio A. Velastin
6.1 What is Traffic Flow Analysis?
131(6)
6.1.1 Traffic Conflicts and Traffic Analysis
131(1)
6.1.2 Time Observation
132(1)
6.1.3 Space Observation
133(1)
6.1.4 The Fundamental Equation
133(1)
6.1.5 The Fundamental Diagram
133(1)
6.1.6 Measuring Traffic Variables
134(1)
6.1.7 Road Counts
135(1)
6.1.8 Junction Counts
135(1)
6.1.9 Passenger Counts
136(1)
6.1.10 Pedestrian Counts
136(1)
6.1.11 Speed Measurement
136(1)
6.2 The Use of Video Analysis in Intelligent Transportation Systems
137(7)
6.2.1 Introduction
137(1)
6.2.2 General Framework for Traffic Flow Analysis
137(1)
6.2.2.1 Foreground Estimation/Segmentation
139(1)
6.2.2.2 Segmentation
140(1)
6.2.2.3 Shadow Removal
140(1)
6.2.2.4 Morphological Operations
141(1)
6.2.2.5 Approaches Based on Object Recognition
141(1)
6.2.2.6 Interest-Point Feature Descriptors
141(1)
6.2.2.7 Appearance Shape-Based Descriptors
142(1)
6.2.2.8 Classification
142(1)
6.2.2.9 Analysis
143(1)
6.2.3 Application Domains
143(1)
6.3 Measuring Traffic Flow from Roadside CCTV Video
144(12)
6.3.1 Video Analysis Framework
144(2)
6.3.2 Vehicle Detection
146(1)
6.3.3 Background Model
146(3)
6.3.4 Counting Vehicles
149(1)
6.3.5 Tracking
150(1)
6.3.6 Camera Calibration
150(2)
6.3.7 Feature Extraction and Vehicle Classification
152(1)
6.3.8 Lane Detection
153(2)
6.3.9 Results
155(1)
6.4 Some Challenges
156(3)
References
159(4)
7 Intersection Monitoring Using Computer Vision Techniques for Capacity, Delay, and Safety Analysis
163(32)
Brendan Tran Morris
Mohammad Shokrolah Shirazi
7.1 Vision-Based Intersection Analysis: Capacity, Delay, and Safety
163(2)
7.1.1 Intersection Monitoring
163(1)
7.1.2 Computer Vision Application
164(1)
7.2 System Overview
165(6)
7.2.1 Tracking Road Users
166(3)
7.2.2 Camera Calibration
169(2)
7.3 Count Analysis
171(2)
7.3.1 Vehicular Counts
171(2)
7.3.2 Nonvehicular Counts
173(1)
7.4 Queue Length Estimation
173(4)
7.4.1 Detection-Based Methods
174(1)
7.4.2 Tracking-Based Methods
175(2)
7.5 Safety Analysis
177(10)
7.5.1 Behaviors
178(1)
7.5.1.1 Turning Prediction
179(1)
7.5.1.2 Abnormality Detection
179(1)
7.5.1.3 Pedestrian Crossing Violation
179(1)
7.5.1.4 Pedestrian Crossing Speed
181(1)
7.5.1.5 Pedestrian Waiting Time
182(1)
7.5.2 Accidents
182(3)
7.5.3 Conflicts
185(2)
7.6 Challenging Problems and Perspectives
187(2)
7.6.1 Robust Detection and Tracking
187(1)
7.6.2 Validity of Prediction Models for Conflict and Collisions
188(1)
7.6.3 Cooperating Sensing Modalities
189(1)
7.6.4 Networked Traffic Monitoring Systems
189(1)
7.7 Conclusion
189(1)
References
190(5)
8 Video-Based Parking Management
195(32)
Oliver Sidla
Yuriy Lipetski
8.1 Introduction
195(2)
8.2 Overview of Parking Sensors
197(3)
8.3 Introduction to Vehicle Occupancy Detection Methods
200(1)
8.4 Monocular Vehicle Detection
200(13)
8.4.1 Advantages of Simple 2D Vehicle Detection
200(1)
8.4.2 Background Model-Based Approaches
200(2)
8.4.3 Vehicle Detection Using Local Feature Descriptors
202(1)
8.4.4 Appearance-Based Vehicle Detection
203(1)
8.4.5 Histograms of Oriented Gradients
204(3)
8.4.6 LBP Features and LBP Histograms
207(1)
8.4.7 Combining Detectors into Cascades and Complex Descriptors
208(1)
8.4.8 Case Study: Parking Space Monitoring Using a Combined Feature Detector
208(3)
8.4.9 Detection Using Artificial Neural Networks
211(2)
8.5 Introduction to Vehicle Detection with 3D Methods
213(2)
8.6 Stereo Vision Methods
215(8)
8.6.1 Introduction to Stereo Methods
215(1)
8.6.2 Limits on the Accuracy of Stereo Reconstruction
216(1)
8.6.3 Computing the Stereo Correspondence
217(1)
8.6.4 Simple Stereo for Volume Occupation Measurement
218(1)
8.6.5 A Practical System for Parking Space Monitoring Using a Stereo System
218(2)
8.6.6 Detection Methods Using Sparse 3D Reconstruction
220(3)
Acknowledgment
223(1)
References
223(4)
9 Video Anomaly Detection
227(30)
Raja Bala
Vishal Monga
9.1 Introduction
227(1)
9.2 Event Encoding
228(5)
9.2.1 Trajectory Descriptors
229(2)
9.2.2 Spatiotemporal Descriptors
231(2)
9.3 Anomaly Detection Models
233(3)
9.3.1 Classification Methods
233(1)
9.3.2 Hidden Markov Models
234(1)
9.3.3 Contextual Methods
234(2)
9.4 Sparse Representation Methods for Robust Video Anomaly Detection
236(17)
9.4.1 Structured Anomaly Detection
237(1)
9.4.1.1 A Joint Sparsity Model for Anomaly Detection
238(1)
9.4.1.2 Supervised Anomaly Detection as Event Classification
242(1)
9.4.1.3 Unsupervised Anomaly Detection via Outlier Rejection
242(1)
9.4.2 Unstructured Video Anomaly Detection
243(2)
9.4.3 Experimental Setup and Results
245(1)
9.4.3.1 Anomaly Detection in Structured Scenarios
246(1)
9.4.3.2 Detection Rates for Single-Object Anomaly Detection
246(1)
9.4.3.3 Detection Rates for Multiple-Object Anomaly Detection
246(1)
9.4.3.4 Anomaly Detection in Unstructured Scenarios
250(3)
9.5 Conclusion and Future Research
253(1)
References
254(3)
Part II Imaging from and within the Vehicle 257(142)
10 Pedestrian Detection
259(24)
Shashank Deshpande
Yang Cai
10.1 Introduction
259(1)
10.2 Overview of the Algorithms
259(1)
10.3 Thermal Imaging
260(1)
10.4 Background Subtraction Methods
261(2)
10.4.1 Frame Subtraction
261(1)
10.4.2 Approximate Median
262(1)
10.4.3 Gaussian Mixture Model
263(1)
10.5 Polar Coordinate Profile
263(2)
10.6 Image-Based Features
265(3)
10.6.1 Histogram of Oriented Gradients
265(1)
10.6.2 Deformable Parts Model
266(1)
10.6.3 LiDAR and Camera Fusion-Based Detection
266(2)
10.7 LiDAR Features
268(12)
10.7.1 Preprocessing Module
268(1)
10.7.2 Feature Extraction Module
268(1)
10.7.3 Fusion Module
268(2)
10.7.4 LIPD Dataset
270(1)
10.7.5 Overview of the Algorithm
270(2)
10.7.6 LiDAR Module
272(3)
10.7.7 Vision Module
275(1)
10.7.8 Results and Discussion
276(1)
10.7.8.1 LiDAR Module
276(1)
10.7.8.2 Vision Module
276(4)
10.8 Summary
280(1)
References
280(3)
11 Lane Detection and Tracking Problems in Lane Departure Warning Systems
283(22)
Gianni Carlo
Alessandro Casavola
Marco Lupia
11.1 Introduction
283(2)
11.1.1 Basic LDWS Algorithm Structure
284(1)
11.2 LD: Algorithms for a Single Frame
285(12)
11.2.1 Image Preprocessing
285(1)
11.2.1.1 Gray-Level Optimization
286(1)
11.2.1.2 Image Smoothing
286(1)
11.2.2 Edge Extraction
287(1)
11.2.2.1 Second-Order Derivative Operators
288(1)
11.2.2.2 Canny's Algorithm
290(1)
11.2.2.3 Comparison of Edge-Detection Algorithms
291(1)
11.2.3 Stripe Identification
291(1)
11.2.3.1 Edge Distribution Function
292(1)
11.2.3.2 Hough Transform
292(2)
11.2.4 Line Fitting
294(1)
11.2.4.1 Linear Fitting
295(1)
11.2.4.2 LP Fitting
295(2)
11.3 LT Algorithms
297(2)
11.3.1 Recursive Filters on Subsequent N frames
298(1)
11.3.2 Kalman Filter
298(1)
11.4 Implementation of an LD and LT Algorithm
299(4)
11.4.1 Simulations
300(1)
11.4.2 Test Driving Scenario
300(1)
11.4.3 Driving Scenario: Lane Departures at Increasing Longitudinal Speed
300(2)
11.4.4 The Proposed Algorithm
302(1)
11.4.5 Conclusions
303(1)
References
303(2)
12 Vision-Based Integrated Techniques for Collision Avoidance Systems
305(16)
Ravi Satzoda
Mohan Trivedi
12.1 Introduction
305(2)
12.2 Related Work
307(1)
12.3 Context Definition for Integrated Approach
307(1)
12.4 ELVIS: Proposed Integrated Approach
308(5)
12.4.1 Vehicle Detection Using Lane Information
309(3)
12.4.2 Improving Lane Detection using On-Road Vehicle Information
312(1)
12.5 Performance Evaluation
313(6)
12.5.1 Vehicle Detection in ELVIS
313(1)
12.5.1.1 Accuracy Analysis
313(1)
12.5.1.2 Computational Efficiency
314(2)
12.5.2 Lane Detection in ELVIS
316(3)
12.6 Concluding Remarks
319(1)
References
319(2)
13 Driver Monitoring
321(22)
Raja Bala
Edgar A. Bernal
13.1 Introduction
321(1)
13.2 Video Acquisition
322(1)
13.3 Face Detection and Alignment
323(2)
13.4 Eye Detection and Analysis
325(1)
13.5 Head Pose and Gaze Estimation
326(6)
13.5.1 Head Pose Estimation
326(2)
13.5.2 Gaze Estimation
328(4)
13.6 Facial Expression Analysis
332(2)
13.7 Multimodal Sensing and Fusion
334(2)
13.8 Conclusions and Future Directions
336(1)
References
337(6)
14 Traffic Sign Detection and Recognition
343(32)
Hasan Fleyeh
14.1 Introduction
343(1)
14.2 Traffic Signs
344(3)
14.2.1 The European Road and Traffic Signs
344(3)
14.2.2 The American Road and Traffic Signs
347(1)
14.3 Traffic Sign Recognition
347(1)
14.4 Traffic Sign Recognition Applications
348(1)
14.5 Potential Challenges
349(1)
14.6 Traffic Sign Recognition System Design
349(20)
14.6.1 Traffic Signs Datasets
352(2)
14.6.2 Colour Segmentation
354(5)
14.6.3 Traffic Sign's Rim Analysis
359(5)
14.6.4 Pictogram Extraction
364(1)
14.6.5 Pictogram Classification Using Features
365(1)
14.6.5.1 Effect of Number of Features
367(1)
14.6.5.2 Classifying Disoriented Traffic Signs
368(1)
14.6.5.3 Training and Testing Time
368(1)
14.7 Working Systems
369(2)
References
371(4)
15 Road Condition Monitoring
375(24)
Matti Kutila
Pasi Pyykonen
Johan Casselgren
Patrik Jonsson
15.1 Introduction
375(1)
15.2 Measurement Principles
376(1)
15.3 Sensor Solutions
377(9)
15.3.1 Camera-Based Friction Estimation Systems
377(2)
15.3.2 Pavement Sensors
379(1)
15.3.3 Spectroscopy
380(2)
15.3.4 Roadside Fog Sensing
382(1)
15.3.5 In-Vehicle Sensors
383(3)
15.4 Classification and Sensor Fusion
386(4)
15.5 Field Studies
390(4)
15.6 Cooperative Road Weather Services
394(1)
15.7 Discussion and Future Work
395(1)
References
396(3)
Index 399
Robert P. Loce, Conduent Labs, USA Dr. Robert P. Loce is a Fellow of SPIE and a Senior Member of IEEE. His publications include a book on enhancement and restoration of digital documents, and 8 book chapters on digital halftoning and digital document processing, 28 refereed journal publications, and 53 conference proceedings. He is currently an associate editor for Journal of Electronic Imaging, where he recently guest-edited a special topic issue on the subject matter of the proposed book.  He also chairs a conference within the SPIE/IS&T Electronic Imaging symposium on the subject matter of the proposed book.  He has also been an associate editor for Real-Time Imaging, and IEEE Transactions on Image Processing.

Raja Bala, Samsung Research America, USA Dr. Bala has authored over 100 publications, including several book chapters, and holds over 120 U.S. patents in the field of digital and color imaging. He has served as adjunct faculty member at the Rochester Institute of Technology, and has taught many short courses and guest lectures on a variety of topics in digital imaging. From 2008-12, he served as Vice President of Publications for the Society for Imaging Science and Technology, where he led the Editorial Board for the IS&T/Wiley Book Series. He has served as Associate Editor of the Journal of Imaging Science and Technology, and is a frequent reviewer for IEEE Transactions on Image Processing, Journal of Electronic Imaging, and Journal of Imaging Science and Technology. Dr. Bala is a Fellow of IS&T and Senior Member of IEEE.

Mohan Trivedi, Jacobs School of Engineering, University of California, San Diego, USA Prof. Mohan Trivedi is the Head of UCSD's Computer Vision and Robotics Research laboratory, overseeing projects such as a robotic, sensor-based traffic-incident monitoring and response system (sponsored by Caltrans). Prof. Trivedi is leading an interdisciplinary effort, as UCSD layer leader for intelligent transportation and telematics for the California Institute for Telecommunications and Information Technology [ Cal-(IT)2]. Prof. Trivedi is a recipient of the Pioneer Award and the Meritorious Service Award from the IEEE Computer Society; and the Distinguished Alumnus Award from Utah State University. He is a Fellow of the International Society for Optical Engineering (SPIE). He is a founding member of the Executive Committee of the UC System-wide Digital Media Innovation Program (DiMI). He is also Editor-in-Chief of Machine Vision & Applications (Springer).   
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