About this Course

90,477 recent views
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level

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

Approx. 31 hours to complete
English

What you will learn

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

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

  • Apply these methods to visual odometry, object detection and tracking

  • Apply semantic segmentation for drivable surface estimation

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level

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

Approx. 31 hours to complete
English

Instructor

Offered by

Placeholder

University of Toronto

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

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

2 hours to complete
4 videos (Total 18 min), 4 readings
4 videos
Welcome to the course4m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
4 readings
Course Prerequisites15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
Recommended Textbooks15m
7 hours to complete

Module 1: Basics of 3D Computer Vision

7 hours to complete
6 videos (Total 43 min), 4 readings, 2 quizzes
6 videos
Lesson 1 Part 2: Camera Projective Geometry8m
Lesson 2: Camera Calibration7m
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7m
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5m
Lesson 4: Image Filtering7m
4 readings
Supplementary Reading: The Camera Sensor30m
Supplementary Reading: Camera Calibration15m
Supplementary Reading: Visual Depth Perception30m
Supplementary Reading: Image Filtering15m
1 practice exercise
Module 1 Graded Quiz30m
Week
2

Week 2

7 hours to complete

Module 2: Visual Features - Detection, Description and Matching

7 hours to complete
6 videos (Total 44 min), 5 readings, 1 quiz
6 videos
Lesson 2: Feature Descriptors6m
Lesson 3 Part 1: Feature Matching7m
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5m
Lesson 4: Outlier Rejection8m
Lesson 5: Visual Odometry9m
5 readings
Supplementary Reading: Feature Detectors and Descriptors30m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Outlier Rejection15m
Supplementary Reading: Visual Odometry10m
Week
3

Week 3

3 hours to complete

Module 3: Feedforward Neural Networks

3 hours to complete
6 videos (Total 58 min), 6 readings, 1 quiz
6 videos
Lesson 2: Output Layers and Loss Functions10m
Lesson 3: Neural Network Training with Gradient Descent10m
Lesson 4: Data Splits and Neural Network Performance Evaluation8m
Lesson 5: Neural Network Regularization9m
Lesson 6: Convolutional Neural Networks9m
6 readings
Supplementary Reading: Feed-Forward Neural Networks15m
Supplementary Reading: Output Layers and Loss Functions15m
Supplementary Reading: Neural Network Training with Gradient Descent15m
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10m
Supplementary Reading: Neural Network Regularization15m
Supplementary Reading: Convolutional Neural Networks10m
1 practice exercise
Feed-Forward Neural Networks30m
Week
4

Week 4

3 hours to complete

Module 4: 2D Object Detection

3 hours to complete
4 videos (Total 52 min), 4 readings, 1 quiz
4 videos
Lesson 2: 2D Object detection with Convolutional Neural Networks11m
Lesson 3: Training vs. Inference11m
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14m
4 readings
Supplementary Reading: The Object Detection Problem15m
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30m
Supplementary Reading: Training vs. Inference45m
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30m
1 practice exercise
Object Detection For Self-Driving Cars30m

Reviews

TOP REVIEWS FROM VISUAL PERCEPTION FOR SELF-DRIVING CARS

View all reviews

About the Self-Driving Cars Specialization

Self-Driving Cars

Frequently Asked Questions

More questions? Visit the Learner Help Center.