课程信息

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第 3 门课程(共 4 门)
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高级

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

完成时间大约为31 小时
英语(English)
字幕:英语(English)

您将学到的内容有

  • 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

可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 3 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
高级

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

完成时间大约为31 小时
英语(English)
字幕:英语(English)

讲师

提供方

多伦多大学 徽标

多伦多大学

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 2 小时

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

完成时间为 2 小时
4 个视频 (总计 18 分钟), 4 个阅读材料
4 个视频
Welcome to the course4分钟
Meet the Instructor, Steven Waslander5分钟
Meet the Instructor, Jonathan Kelly2分钟
4 个阅读材料
Course Prerequisites15分钟
How to Use Discussion Forums15分钟
How to Use Supplementary Readings in This Course15分钟
Recommended Textbooks15分钟
完成时间为 7 小时

Module 1: Basics of 3D Computer Vision

完成时间为 7 小时
6 个视频 (总计 43 分钟), 4 个阅读材料, 2 个测验
6 个视频
Lesson 1 Part 2: Camera Projective Geometry8分钟
Lesson 2: Camera Calibration7分钟
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7分钟
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5分钟
Lesson 4: Image Filtering7分钟
4 个阅读材料
Supplementary Reading: The Camera Sensor30分钟
Supplementary Reading: Camera Calibration15分钟
Supplementary Reading: Visual Depth Perception30分钟
Supplementary Reading: Image Filtering15分钟
1 个练习
Module 1 Graded Quiz30分钟
2

2

完成时间为 7 小时

Module 2: Visual Features - Detection, Description and Matching

完成时间为 7 小时
6 个视频 (总计 44 分钟), 5 个阅读材料, 1 个测验
6 个视频
Lesson 2: Feature Descriptors6分钟
Lesson 3 Part 1: Feature Matching7分钟
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5分钟
Lesson 4: Outlier Rejection8分钟
Lesson 5: Visual Odometry9分钟
5 个阅读材料
Supplementary Reading: Feature Detectors and Descriptors30分钟
Supplementary Reading: Feature Matching15分钟
Supplementary Reading: Feature Matching15分钟
Supplementary Reading: Outlier Rejection15分钟
Supplementary Reading: Visual Odometry10分钟
3

3

完成时间为 3 小时

Module 3: Feedforward Neural Networks

完成时间为 3 小时
6 个视频 (总计 58 分钟), 6 个阅读材料, 1 个测验
6 个视频
Lesson 2: Output Layers and Loss Functions10分钟
Lesson 3: Neural Network Training with Gradient Descent10分钟
Lesson 4: Data Splits and Neural Network Performance Evaluation8分钟
Lesson 5: Neural Network Regularization9分钟
Lesson 6: Convolutional Neural Networks9分钟
6 个阅读材料
Supplementary Reading: Feed-Forward Neural Networks15分钟
Supplementary Reading: Output Layers and Loss Functions15分钟
Supplementary Reading: Neural Network Training with Gradient Descent15分钟
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10分钟
Supplementary Reading: Neural Network Regularization15分钟
Supplementary Reading: Convolutional Neural Networks10分钟
1 个练习
Feed-Forward Neural Networks30分钟
4

4

完成时间为 3 小时

Module 4: 2D Object Detection

完成时间为 3 小时
4 个视频 (总计 52 分钟), 4 个阅读材料, 1 个测验
4 个视频
Lesson 2: 2D Object detection with Convolutional Neural Networks11分钟
Lesson 3: Training vs. Inference11分钟
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14分钟
4 个阅读材料
Supplementary Reading: The Object Detection Problem15分钟
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30分钟
Supplementary Reading: Training vs. Inference45分钟
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30分钟
1 个练习
Object Detection For Self-Driving Cars30分钟

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关于 自动驾驶汽车 专项课程

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)....
自动驾驶汽车

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