Hands-On Vision and Behavior for Self-Driving Cars :: Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4.
This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python. |
| Beschreibung: | 1 online resource (374 pages) |
| ISBN: | 1800201931 9781800201934 |
Internformat
MARC
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| 100 | 1 | |a Venturi, Luca. | |
| 245 | 1 | 0 | |a Hands-On Vision and Behavior for Self-Driving Cars : |b Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2020. | ||
| 300 | |a 1 online resource (374 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Print version record. | |
| 520 | |a This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python. | ||
| 505 | 0 | |a Cover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: OpenCV and Sensors and Signals -- Chapter 1: OpenCV Basics and Camera Calibration -- Technical requirements -- Introduction to OpenCV and NumPy -- OpenCV and NumPy -- Image size -- Grayscale images -- RGB images -- Working with image files -- Working with video files -- Working with webcams -- Manipulating images -- Flipping an image -- Blurring an image -- Changing contrast, brightness, and gamma -- Drawing rectangles and text -- Pedestrian detection using HOG -- Sliding window | |
| 505 | 8 | |a Using HOG with OpenCV -- Introduction to the camera -- Camera terminology -- The components of a camera -- Considerations for choosing a camera -- Strengths and weaknesses of cameras -- Camera calibration with OpenCV -- Distortion detection -- Calibration -- Summary -- Questions -- Chapter 2: Understanding and Working with Signals -- Technical requirements -- Understanding signal types -- Analog versus digital -- Serial versus parallel -- Universal Asynchronous Receive and Transmit (UART) -- Differential versus single-ended -- I2C -- SPI -- Framed-based serial protocols -- Understanding CAN | |
| 505 | 8 | |a Ethernet and internet protocols -- Understanding UDP -- Understanding TCP -- Summary -- Questions -- Further reading -- Open source protocol tools -- Chapter 3: Lane Detection -- Technical requirements -- How to perform thresholding -- How thresholding works on different color spaces -- RGB/BGR -- HLS -- HSV -- LAB -- YCbCr -- Our choice -- Perspective correction -- Edge detection -- Interpolated threshold -- Combined threshold -- Finding the lanes using histograms -- The sliding window algorithm -- Initialization -- Coordinates of the sliding windows -- Polynomial fitting -- Enhancing a video | |
| 505 | 8 | |a Partial histogram -- Rolling average -- Summary -- Questions -- Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks -- Chapter 4: Deep Learning with Neural Networks -- Technical requirements -- Understanding machine learning and neural networks -- Neural networks -- Neurons -- Parameters -- The success of deep learning -- Learning about convolutional neural networks -- Convolutions -- Why are convolutions so great? -- Getting started with Keras and TensorFlow -- Requirements -- Detecting MNIST handwritten digits -- What did we just load? | |
| 505 | 8 | |a Training samples and labels -- One-hot encoding -- Training and testing datasets -- Defining the model of the neural network -- LeNet -- The code -- The architecture -- Training a neural network -- CIFAR-10 -- Summary -- Questions -- Further reading -- Chapter 5: Deep Learning Workflow -- Technical requirements -- Obtaining the dataset -- Datasets in the Keras module -- Existing datasets -- Your custom dataset -- Understanding the three datasets -- Splitting the dataset -- Understanding classifiers -- Creating a real-world dataset -- Data augmentation -- The model -- Tuning convolutional layers | |
| 650 | 0 | |a Automated vehicles |x Computer programs. | |
| 650 | 0 | |a Computer vision. |0 http://id.loc.gov/authorities/subjects/sh85029549 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 0 | |a OpenCV (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2016000128 | |
| 650 | 6 | |a Véhicules autonomes |x Logiciels. | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a OpenCV (Langage de programmation) | |
| 650 | 7 | |a Computer vision |2 fast | |
| 650 | 7 | |a OpenCV (Computer program language) |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a Korda, Krishtof. | |
| 758 | |i has work: |a Hands-On Vision and Behavior for Self-Driving Cars (Text) |1 https://id.oclc.org/worldcat/entity/E39PCXfVM6J6YpcDK3fBbJMxV3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Venturi, Luca. |t Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |d Birmingham : Packt Publishing, Limited, ©2020 |z 9781800203587 |
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| callnumber-subject | TL - Motor Vehicles and Aeronautics |
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| contents | Cover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: OpenCV and Sensors and Signals -- Chapter 1: OpenCV Basics and Camera Calibration -- Technical requirements -- Introduction to OpenCV and NumPy -- OpenCV and NumPy -- Image size -- Grayscale images -- RGB images -- Working with image files -- Working with video files -- Working with webcams -- Manipulating images -- Flipping an image -- Blurring an image -- Changing contrast, brightness, and gamma -- Drawing rectangles and text -- Pedestrian detection using HOG -- Sliding window Using HOG with OpenCV -- Introduction to the camera -- Camera terminology -- The components of a camera -- Considerations for choosing a camera -- Strengths and weaknesses of cameras -- Camera calibration with OpenCV -- Distortion detection -- Calibration -- Summary -- Questions -- Chapter 2: Understanding and Working with Signals -- Technical requirements -- Understanding signal types -- Analog versus digital -- Serial versus parallel -- Universal Asynchronous Receive and Transmit (UART) -- Differential versus single-ended -- I2C -- SPI -- Framed-based serial protocols -- Understanding CAN Ethernet and internet protocols -- Understanding UDP -- Understanding TCP -- Summary -- Questions -- Further reading -- Open source protocol tools -- Chapter 3: Lane Detection -- Technical requirements -- How to perform thresholding -- How thresholding works on different color spaces -- RGB/BGR -- HLS -- HSV -- LAB -- YCbCr -- Our choice -- Perspective correction -- Edge detection -- Interpolated threshold -- Combined threshold -- Finding the lanes using histograms -- The sliding window algorithm -- Initialization -- Coordinates of the sliding windows -- Polynomial fitting -- Enhancing a video Partial histogram -- Rolling average -- Summary -- Questions -- Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks -- Chapter 4: Deep Learning with Neural Networks -- Technical requirements -- Understanding machine learning and neural networks -- Neural networks -- Neurons -- Parameters -- The success of deep learning -- Learning about convolutional neural networks -- Convolutions -- Why are convolutions so great? -- Getting started with Keras and TensorFlow -- Requirements -- Detecting MNIST handwritten digits -- What did we just load? Training samples and labels -- One-hot encoding -- Training and testing datasets -- Defining the model of the neural network -- LeNet -- The code -- The architecture -- Training a neural network -- CIFAR-10 -- Summary -- Questions -- Further reading -- Chapter 5: Deep Learning Workflow -- Technical requirements -- Obtaining the dataset -- Datasets in the Keras module -- Existing datasets -- Your custom dataset -- Understanding the three datasets -- Splitting the dataset -- Understanding classifiers -- Creating a real-world dataset -- Data augmentation -- The model -- Tuning convolutional layers |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
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| dewey-search | 629.046028637 |
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| dewey-tens | 620 - Engineering and allied operations |
| discipline | Verkehr / Transport |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:06Z |
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| isbn | 1800201931 9781800201934 |
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| spelling | Venturi, Luca. Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. Birmingham : Packt Publishing, Limited, 2020. 1 online resource (374 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python. Cover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: OpenCV and Sensors and Signals -- Chapter 1: OpenCV Basics and Camera Calibration -- Technical requirements -- Introduction to OpenCV and NumPy -- OpenCV and NumPy -- Image size -- Grayscale images -- RGB images -- Working with image files -- Working with video files -- Working with webcams -- Manipulating images -- Flipping an image -- Blurring an image -- Changing contrast, brightness, and gamma -- Drawing rectangles and text -- Pedestrian detection using HOG -- Sliding window Using HOG with OpenCV -- Introduction to the camera -- Camera terminology -- The components of a camera -- Considerations for choosing a camera -- Strengths and weaknesses of cameras -- Camera calibration with OpenCV -- Distortion detection -- Calibration -- Summary -- Questions -- Chapter 2: Understanding and Working with Signals -- Technical requirements -- Understanding signal types -- Analog versus digital -- Serial versus parallel -- Universal Asynchronous Receive and Transmit (UART) -- Differential versus single-ended -- I2C -- SPI -- Framed-based serial protocols -- Understanding CAN Ethernet and internet protocols -- Understanding UDP -- Understanding TCP -- Summary -- Questions -- Further reading -- Open source protocol tools -- Chapter 3: Lane Detection -- Technical requirements -- How to perform thresholding -- How thresholding works on different color spaces -- RGB/BGR -- HLS -- HSV -- LAB -- YCbCr -- Our choice -- Perspective correction -- Edge detection -- Interpolated threshold -- Combined threshold -- Finding the lanes using histograms -- The sliding window algorithm -- Initialization -- Coordinates of the sliding windows -- Polynomial fitting -- Enhancing a video Partial histogram -- Rolling average -- Summary -- Questions -- Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks -- Chapter 4: Deep Learning with Neural Networks -- Technical requirements -- Understanding machine learning and neural networks -- Neural networks -- Neurons -- Parameters -- The success of deep learning -- Learning about convolutional neural networks -- Convolutions -- Why are convolutions so great? -- Getting started with Keras and TensorFlow -- Requirements -- Detecting MNIST handwritten digits -- What did we just load? Training samples and labels -- One-hot encoding -- Training and testing datasets -- Defining the model of the neural network -- LeNet -- The code -- The architecture -- Training a neural network -- CIFAR-10 -- Summary -- Questions -- Further reading -- Chapter 5: Deep Learning Workflow -- Technical requirements -- Obtaining the dataset -- Datasets in the Keras module -- Existing datasets -- Your custom dataset -- Understanding the three datasets -- Splitting the dataset -- Understanding classifiers -- Creating a real-world dataset -- Data augmentation -- The model -- Tuning convolutional layers Automated vehicles Computer programs. Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 OpenCV (Computer program language) http://id.loc.gov/authorities/subjects/sh2016000128 Véhicules autonomes Logiciels. Vision par ordinateur. Python (Langage de programmation) OpenCV (Langage de programmation) Computer vision fast OpenCV (Computer program language) fast Python (Computer program language) fast Korda, Krishtof. has work: Hands-On Vision and Behavior for Self-Driving Cars (Text) https://id.oclc.org/worldcat/entity/E39PCXfVM6J6YpcDK3fBbJMxV3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Venturi, Luca. Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. Birmingham : Packt Publishing, Limited, ©2020 9781800203587 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2659431 Volltext |
| spellingShingle | Venturi, Luca Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. Cover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: OpenCV and Sensors and Signals -- Chapter 1: OpenCV Basics and Camera Calibration -- Technical requirements -- Introduction to OpenCV and NumPy -- OpenCV and NumPy -- Image size -- Grayscale images -- RGB images -- Working with image files -- Working with video files -- Working with webcams -- Manipulating images -- Flipping an image -- Blurring an image -- Changing contrast, brightness, and gamma -- Drawing rectangles and text -- Pedestrian detection using HOG -- Sliding window Using HOG with OpenCV -- Introduction to the camera -- Camera terminology -- The components of a camera -- Considerations for choosing a camera -- Strengths and weaknesses of cameras -- Camera calibration with OpenCV -- Distortion detection -- Calibration -- Summary -- Questions -- Chapter 2: Understanding and Working with Signals -- Technical requirements -- Understanding signal types -- Analog versus digital -- Serial versus parallel -- Universal Asynchronous Receive and Transmit (UART) -- Differential versus single-ended -- I2C -- SPI -- Framed-based serial protocols -- Understanding CAN Ethernet and internet protocols -- Understanding UDP -- Understanding TCP -- Summary -- Questions -- Further reading -- Open source protocol tools -- Chapter 3: Lane Detection -- Technical requirements -- How to perform thresholding -- How thresholding works on different color spaces -- RGB/BGR -- HLS -- HSV -- LAB -- YCbCr -- Our choice -- Perspective correction -- Edge detection -- Interpolated threshold -- Combined threshold -- Finding the lanes using histograms -- The sliding window algorithm -- Initialization -- Coordinates of the sliding windows -- Polynomial fitting -- Enhancing a video Partial histogram -- Rolling average -- Summary -- Questions -- Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks -- Chapter 4: Deep Learning with Neural Networks -- Technical requirements -- Understanding machine learning and neural networks -- Neural networks -- Neurons -- Parameters -- The success of deep learning -- Learning about convolutional neural networks -- Convolutions -- Why are convolutions so great? -- Getting started with Keras and TensorFlow -- Requirements -- Detecting MNIST handwritten digits -- What did we just load? Training samples and labels -- One-hot encoding -- Training and testing datasets -- Defining the model of the neural network -- LeNet -- The code -- The architecture -- Training a neural network -- CIFAR-10 -- Summary -- Questions -- Further reading -- Chapter 5: Deep Learning Workflow -- Technical requirements -- Obtaining the dataset -- Datasets in the Keras module -- Existing datasets -- Your custom dataset -- Understanding the three datasets -- Splitting the dataset -- Understanding classifiers -- Creating a real-world dataset -- Data augmentation -- The model -- Tuning convolutional layers Automated vehicles Computer programs. Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 OpenCV (Computer program language) http://id.loc.gov/authorities/subjects/sh2016000128 Véhicules autonomes Logiciels. Vision par ordinateur. Python (Langage de programmation) OpenCV (Langage de programmation) Computer vision fast OpenCV (Computer program language) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85029549 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh2016000128 |
| title | Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| title_auth | Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| title_exact_search | Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| title_full | Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| title_fullStr | Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| title_full_unstemmed | Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| title_short | Hands-On Vision and Behavior for Self-Driving Cars : |
| title_sort | hands on vision and behavior for self driving cars explore visual perception lane detection and object classification with python 3 and opencv 4 |
| title_sub | Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4. |
| topic | Automated vehicles Computer programs. Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 OpenCV (Computer program language) http://id.loc.gov/authorities/subjects/sh2016000128 Véhicules autonomes Logiciels. Vision par ordinateur. Python (Langage de programmation) OpenCV (Langage de programmation) Computer vision fast OpenCV (Computer program language) fast Python (Computer program language) fast |
| topic_facet | Automated vehicles Computer programs. Computer vision. Python (Computer program language) OpenCV (Computer program language) Véhicules autonomes Logiciels. Vision par ordinateur. Python (Langage de programmation) OpenCV (Langage de programmation) Computer vision |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2659431 |
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