À propos de ce cours
4.3
13 notes
1 avis

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

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

Approx. 15 heures pour terminer

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

Anglais

Sous-titres : Anglais

Ce que vous allez apprendre

  • Check

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

  • Check

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

  • Check

    Apply these methods to visual odometry, object detection and tracking

  • Check

    Apply semantic segmentation for drivable surface estimation

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau avancé

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

Approx. 15 heures pour terminer

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

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
2 heures pour terminer

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 vidéos (Total 18 min), 4 lectures
4 vidéos
Welcome to the course4 min
Meet the Instructor, Steven Waslander5 min
Meet the Instructor, Jonathan Kelly2 min
4 lectures
Course Prerequisites15 min
How to Use Discussion Forums15 min
How to Use Supplementary Readings in This Course15 min
Recommended Textbooks15 min
7 heures pour terminer

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 vidéos (Total 43 min), 4 lectures, 2 quiz
6 vidéos
Lesson 1 Part 2: Camera Projective Geometry8 min
Lesson 2: Camera Calibration7 min
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7 min
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5 min
Lesson 4: Image Filtering7 min
4 lectures
Supplementary Reading: The Camera Sensor30 min
Supplementary Reading: Camera Calibration15 min
Supplementary Reading: Visual Depth Perception30 min
Supplementary Reading: Image Filtering15 min
1 exercice pour s'entraîner
Module 1 Graded Quiz30 min
Semaine
2
7 heures pour terminer

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 vidéos (Total 44 min), 5 lectures, 1 quiz
6 vidéos
Lesson 2: Feature Descriptors6 min
Lesson 3 Part 1: Feature Matching7 min
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5 min
Lesson 4: Outlier Rejection8 min
Lesson 5: Visual Odometry9 min
5 lectures
Supplementary Reading: Feature Detectors and Descriptors30 min
Supplementary Reading: Feature Matching15 min
Supplementary Reading: Feature Matching15 min
Supplementary Reading: Outlier Rejection15 min
Supplementary Reading: Visual Odometry10 min
Semaine
3
3 heures pour terminer

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 vidéos (Total 58 min), 6 lectures, 1 quiz
6 vidéos
Lesson 2: Output Layers and Loss Functions10 min
Lesson 3: Neural Network Training with Gradient Descent10 min
Lesson 4: Data Splits and Neural Network Performance Evaluation8 min
Lesson 5: Neural Network Regularization9 min
Lesson 6: Convolutional Neural Networks9 min
6 lectures
Supplementary Reading: Feed-Forward Neural Networks15 min
Supplementary Reading: Output Layers and Loss Functions15 min
Supplementary Reading: Neural Network Training with Gradient Descent15 min
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10 min
Supplementary Reading: Neural Network Regularization15 min
Supplementary Reading: Convolutional Neural Networks10 min
1 exercice pour s'entraîner
Feed-Forward Neural Networks30 min
Semaine
4
3 heures pour terminer

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 vidéos (Total 52 min), 4 lectures, 1 quiz
4 vidéos
Lesson 2: 2D Object detection with Convolutional Neural Networks11 min
Lesson 3: Training vs. Inference11 min
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14 min
4 lectures
Supplementary Reading: The Object Detection Problem15 min
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30 min
Supplementary Reading: Training vs. Inference45 min
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30 min
1 exercice pour s'entraîner
Object Detection For Self-Driving Cars30 min

Enseignant

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

À propos de Université 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. ...

À propos de la Spécialisation Voiture autonome

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)....
Voiture autonome

Foire Aux Questions

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