About the book

“The readers can gain practical knowledge of computer vision while detecting vehicles.  The book starts from an Introductory to Intermediate levels of algorithms for the detection of vehicles using computer vision.  Computer Vision is an exciting area as it gives improved graphical data for human understanding and processing of image data for machine perception as required for an application.  More specifically the video processing has become a spot light for surveillance due to less expensive, high performance, can capture larger area, capture fast moving vehicles, provide visual information for manual inspection whenever necessary and more traffic information can be acquired by surveillance cameras.  To have a smart Intelligent Traffic Management System it’s important to detect vehicle on road and analyses the traffic flow.  Existing traffic flow.  Existing traffic analysis module makes use of a dedicated hardware and processors, thus disrupting the regular flow of traffic for implementation.  Gathering of traffic information, vehicle flow statistics help traffic control department to from a road network planning, hence a vision-based technology is best suited to carryout Intelligent Traffic Management System in a smarter way. Different features such as motion, colour, corner etc of vehicles are considered and discussed to understand from basic to advanced techniques of computer vision in  detection of vehicles, tracking and counting.  The book discusses novel algorithms that focus on vehicle detection and determining vehicle density.”    

Contents:

Chapter 1 : Introduction

1.1 Digital Image/Video Processing

1.2 Video Processing Applications

1.3 Image Processing Techniques

1.4 Morphological Operations

1.4.1 Morphological Transformation

1.5 Object tracking 

1.5.1 Data Association

1.6 Motivation

1.7 Problem Statement

1.8 Objectives

1.9 Chooice of Cameras

1.10 Performance

1.11 Organization of Book

Chapter 2 : Literature Survey

2.1 Existing

2.2 Video Monitoring Systems

Chapter 3 : Improvised Approach for Vehicle Detection and Counting

3.1 Introduction

3.2 Proposed Methodology

3.2.1 Background Subtraction

3.2.2 Connected Component Labeling

3.2.3 Centroid 

3.2.4 Counting

3.3 Results and Conclusion

3.3.1 Error Related

3.3.2 Computational Cost

3.4 Summary

Chapter 4 : A Novel Vehicle Detection Method Based on Morphological Operations

4.1 Introduction

4.2 Proposed Method

4.2.1 Separation of Channels

4.2.2 Transformation

4.2.3 Background Substraction

4.2.4 Connected Component Labeling

4.2.5 Post Processing

4.3 Results and Discussion

4.3.1 Comparative Study

4.4 Summary

Chapter 5 : Vehicle Detection Based on Color

5.1 Introduction

5.2 Proposed Methodology 

5.2.1 Bob Analysis 

5.2.2 Convex Hull

5.3 Results and Discussion

5.4 Summary 

Chapter 6 : An Approach to Detect Vehicles on Corner Points 

6.1 Introduction 

6.2 Proposed Methodology 

6.2.1 Corner Detection

6.2.2 Background,  Subtraction

6.2.3 Grouping of corner points 

6.2.4 Tracking 

6.3 Results and Discussion 

6.3.1 Data 

6.3.2 Detection in rainy Climate 

6.3.3 Detection in sunny day with Low Resolution

6.3.4 Detection in sunny day High Resolution 

6.3.5 Detection in Fog video 

6.3.6 Rearview Vehicle Detection with moving camera 

6.3.7 Rear view multiple Vehicle Detection with moving camera 

6.3.8 Computational Cost

6.3.9 Performance Evaluation

6.3.10 Comparative Studies 

6.3.11 Detection Error Analysis 

6.4 Summary

Chapter 7 : Real-time Vehicle Detection and Tracking

7.1 Introduction 

7.2 Proposed Methodology

7.2.1 Centroid

7.2.2 Data Association

7.2.3 Cost Matrix 

7.2.4 The Hungarian Algorithm 

7.2.5 Kalman Filter 

7.3 Results and Discussion 

7.3.1 Performance 

7.3.2 Computational Cost

7.3.3 Detection Error Analysis

7.3.4 Comparative Study

7.3.5 Reciever Operative Characteristics (ROC)

7.4 Summary

Chapter 8 : A Novel Approach in Real-time Vehicle Detection and Tracking Using Raspberry Pi

8.1 Introduction 

8.2 Proposed Methodology 

8.2.1 Respberry Pi with Pi Camera

8.3 Results and Discussion 

8.3.1 Performance 

8.3.2 Computational Cost

8.4 Summary

Chapter 9 : Conclusion and Future Scope 

Biblography

Appendix