Об этом курсе
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Продвинутый уровень

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

Прибл. 20 часа на выполнение

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

Английский

Субтитры: Английский

Чему вы научитесь

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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% онлайн

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Продвинутый уровень

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

Прибл. 20 часа на выполнение

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

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
2 ч. на завершение

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 видео ((всего 18 мин.)), 4 материалов для самостоятельного изучения
4 видео
Welcome to the course4мин
Meet the Instructor, Steven Waslander5мин
Meet the Instructor, Jonathan Kelly2мин
4 материала для самостоятельного изучения
Course Prerequisites15мин
How to Use Discussion Forums15мин
How to Use Supplementary Readings in This Course15мин
Recommended Textbooks15мин
7 ч. на завершение

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 видео ((всего 43 мин.)), 4 материалов для самостоятельного изучения, 2 тестов
6 видео
Lesson 1 Part 2: Camera Projective Geometry8мин
Lesson 2: Camera Calibration7мин
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7мин
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5мин
Lesson 4: Image Filtering7мин
4 материала для самостоятельного изучения
Supplementary Reading: The Camera Sensor30мин
Supplementary Reading: Camera Calibration15мин
Supplementary Reading: Visual Depth Perception30мин
Supplementary Reading: Image Filtering15мин
1 практическое упражнение
Module 1 Graded Quiz30мин
Неделя
2
7 ч. на завершение

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 видео ((всего 44 мин.)), 5 материалов для самостоятельного изучения, 1 тест
6 видео
Lesson 2: Feature Descriptors6мин
Lesson 3 Part 1: Feature Matching7мин
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5мин
Lesson 4: Outlier Rejection8мин
Lesson 5: Visual Odometry9мин
5 материала для самостоятельного изучения
Supplementary Reading: Feature Detectors and Descriptors30мин
Supplementary Reading: Feature Matching15мин
Supplementary Reading: Feature Matching15мин
Supplementary Reading: Outlier Rejection15мин
Supplementary Reading: Visual Odometry10мин
Неделя
3
3 ч. на завершение

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 видео ((всего 58 мин.)), 6 материалов для самостоятельного изучения, 1 тест
6 видео
Lesson 2: Output Layers and Loss Functions10мин
Lesson 3: Neural Network Training with Gradient Descent10мин
Lesson 4: Data Splits and Neural Network Performance Evaluation8мин
Lesson 5: Neural Network Regularization9мин
Lesson 6: Convolutional Neural Networks9мин
6 материала для самостоятельного изучения
Supplementary Reading: Feed-Forward Neural Networks15мин
Supplementary Reading: Output Layers and Loss Functions15мин
Supplementary Reading: Neural Network Training with Gradient Descent15мин
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10мин
Supplementary Reading: Neural Network Regularization15мин
Supplementary Reading: Convolutional Neural Networks10мин
1 практическое упражнение
Feed-Forward Neural Networks30мин
Неделя
4
3 ч. на завершение

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 видео ((всего 52 мин.)), 4 материалов для самостоятельного изучения, 1 тест
4 видео
Lesson 2: 2D Object detection with Convolutional Neural Networks11мин
Lesson 3: Training vs. Inference11мин
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14мин
4 материала для самостоятельного изучения
Supplementary Reading: The Object Detection Problem15мин
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30мин
Supplementary Reading: Training vs. Inference45мин
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30мин
1 практическое упражнение
Object Detection For Self-Driving Cars30мин

Преподаватели

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

О Торонтский университет

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. ...

О специализации ''Беспилотные автомобили'

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)....
Беспилотные автомобили

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