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Hands-On Vision and Behavior for Self-Driving Cars

You're reading from   Hands-On Vision and Behavior for Self-Driving Cars Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800203587
Length 374 pages
Edition 1st Edition
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Authors (2):
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Krishtof Korda Krishtof Korda
Author Profile Icon Krishtof Korda
Krishtof Korda
Luca Venturi Luca Venturi
Author Profile Icon Luca Venturi
Luca Venturi
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Table of Contents (17) Chapters Close

Preface 1. Section 1: OpenCV and Sensors and Signals
2. Chapter 1: OpenCV Basics and Camera Calibration FREE CHAPTER 3. Chapter 2: Understanding and Working with Signals 4. Chapter 3: Lane Detection 5. Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
6. Chapter 4: Deep Learning with Neural Networks 7. Chapter 5: Deep Learning Workflow 8. Chapter 6: Improving Your Neural Network 9. Chapter 7: Detecting Pedestrians and Traffic Lights 10. Chapter 8: Behavioral Cloning 11. Chapter 9: Semantic Segmentation 12. Section 3: Mapping and Controls
13. Chapter 10: Steering, Throttle, and Brake Control 14. Chapter 11: Mapping Our Environments 15. Assessments 16. Other Books You May Enjoy

Perspective correction

Let's take a step back and start simple. The easiest case that we can have is with a straight lane. Let's see how it looks:

Figure 3.13 – Straight lane, from Speed Dreams

Figure 3.13 – Straight lane, from Speed Dreams

If we were flying over the road, and watching it from a bird's eye view, the lanes would be parallel, but in the picture, they are not, because of the perspective.

The perspective depends on the focal length of the lens (lenses with a shorter focal length show a stronger perspective) and the position of the camera. Once the camera is mounted on a car, the perspective is fixed, so we can take it into consideration and correct the image.

OpenCV has a method to compute the perspective transformation: getPerspectiveTransform().

It takes two parameters, both arrays of four points, identifying the trapezoid of the perspective. One array is the source and one array is the destination. This means that the same method can be used to compute...

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