Informações sobre o curso
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Nível avançado

This is an advanced course, intended for learners with a background in computer vision and deep learning.

Aprox. 15 horas para completar

Sugerido: 6 weeks of study, 5-6 hours per week...

Inglês

Legendas: Inglês

O que você vai aprender

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    Work with the pinhole camera model, and perform intrinsic and extrinsic camera calibration

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    Detect, describe and match image features and design your own convolutional neural networks

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    Apply these methods to visual odometry, object detection and tracking

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    Apply semantic segmentation for drivable surface estimation

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

This is an advanced course, intended for learners with a background in computer vision and deep learning.

Aprox. 15 horas para completar

Sugerido: 6 weeks of study, 5-6 hours per week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
2 horas para concluir

Welcome to Course 3: Visual Perception for Self-Driving Cars

This module introduces the main concepts from the broad field of computer vision needed to progress through perception methods for self-driving vehicles. The main components include camera models and their calibration, monocular and stereo vision, projective geometry, and convolution operations....
4 vídeos (total de (Total 18 mín.) min), 4 leituras
4 videos
Welcome to the course4min
Meet the Instructor, Steven Waslander5min
Meet the Instructor, Jonathan Kelly2min
4 leituras
Course Prerequisites15min
How to Use Discussion Forums15min
How to Use Supplementary Readings in This Course15min
Recommended Textbooks15min
7 horas para concluir

Module 1: Basics of 3D Computer Vision

This module introduces the main concepts from the broad field of computer vision needed to progress through perception methods for self-driving vehicles. The main components include camera models and their calibration, monocular and stereo vision, projective geometry, and convolution operations....
6 vídeos (total de (Total 43 mín.) min), 4 leituras, 2 testes
6 videos
Lesson 1 Part 2: Camera Projective Geometry8min
Lesson 2: Camera Calibration7min
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7min
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5min
Lesson 4: Image Filtering7min
4 leituras
Supplementary Reading: The Camera Sensor30min
Supplementary Reading: Camera Calibration15min
Supplementary Reading: Visual Depth Perception30min
Supplementary Reading: Image Filtering15min
1 exercício prático
Module 1 Graded Quiz30min
Semana
2
7 horas para concluir

Module 2: Visual Features - Detection, Description and Matching

Visual features are used to track motion through an environment and to recognize places in a map. This module describes how features can be detected and tracked through a sequence of images and fused with other sources for localization as described in Course 2. Feature extraction is also fundamental to object detection and semantic segmentation in deep networks, and this module introduces some of the feature detection methods employed in that context as well....
6 vídeos (total de (Total 44 mín.) min), 5 leituras, 1 teste
6 videos
Lesson 2: Feature Descriptors6min
Lesson 3 Part 1: Feature Matching7min
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5min
Lesson 4: Outlier Rejection8min
Lesson 5: Visual Odometry9min
5 leituras
Supplementary Reading: Feature Detectors and Descriptors30min
Supplementary Reading: Feature Matching15min
Supplementary Reading: Feature Matching15min
Supplementary Reading: Outlier Rejection15min
Supplementary Reading: Visual Odometry10min
Semana
3
3 horas para concluir

Module 3: Feedforward Neural Networks

Deep learning is a core enabling technology for self-driving perception. This module briefly introduces the core concepts employed in modern convolutional neural networks, with an emphasis on methods that have been proven to be effective for tasks such as object detection and semantic segmentation. Basic network architectures, common components and helpful tools for constructing and training networks are described....
6 vídeos (total de (Total 58 mín.) min), 6 leituras, 1 teste
6 videos
Lesson 2: Output Layers and Loss Functions10min
Lesson 3: Neural Network Training with Gradient Descent10min
Lesson 4: Data Splits and Neural Network Performance Evaluation8min
Lesson 5: Neural Network Regularization9min
Lesson 6: Convolutional Neural Networks9min
6 leituras
Supplementary Reading: Feed-Forward Neural Networks15min
Supplementary Reading: Output Layers and Loss Functions15min
Supplementary Reading: Neural Network Training with Gradient Descent15min
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10min
Supplementary Reading: Neural Network Regularization15min
Supplementary Reading: Convolutional Neural Networks10min
1 exercício prático
Feed-Forward Neural Networks30min
Semana
4
3 horas para concluir

Module 4: 2D Object Detection

The two most prevalent applications of deep neural networks to self-driving are object detection, including pedestrian, cyclists and vehicles, and semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. This module presents baseline techniques for object detection and the following module introduce semantic segmentation, both of which can be used to create a complete self-driving car perception pipeline....
4 vídeos (total de (Total 52 mín.) min), 4 leituras, 1 teste
4 videos
Lesson 2: 2D Object detection with Convolutional Neural Networks11min
Lesson 3: Training vs. Inference11min
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14min
4 leituras
Supplementary Reading: The Object Detection Problem15min
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30min
Supplementary Reading: Training vs. Inference45min
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30min
1 exercício prático
Object Detection For Self-Driving Cars30min

Instrutores

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

Sobre Universidade de Toronto

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

Sobre o Programa de cursos integrados Carros autoguiáveis

Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....
Carros autoguiáveis

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