課程信息
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第 3 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

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高級

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

完成時間大約為20 小時

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

英語(English)

字幕:英語(English)

您將學到的內容有

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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

第 3 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

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

完成時間大約為20 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 2 小時

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

4 個視頻 (總計 18 分鐘), 4 個閱讀材料
4 個視頻
Welcome to the course4分鐘
Meet the Instructor, Steven Waslander5分鐘
Meet the Instructor, Jonathan Kelly2分鐘
4 個閱讀材料
Course Prerequisites15分鐘
How to Use Discussion Forums15分鐘
How to Use Supplementary Readings in This Course15分鐘
Recommended Textbooks15分鐘
完成時間為 7 小時

Module 1: Basics of 3D Computer Vision

6 個視頻 (總計 43 分鐘), 4 個閱讀材料, 2 個測驗
6 個視頻
Lesson 1 Part 2: Camera Projective Geometry8分鐘
Lesson 2: Camera Calibration7分鐘
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7分鐘
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5分鐘
Lesson 4: Image Filtering7分鐘
4 個閱讀材料
Supplementary Reading: The Camera Sensor30分鐘
Supplementary Reading: Camera Calibration15分鐘
Supplementary Reading: Visual Depth Perception30分鐘
Supplementary Reading: Image Filtering15分鐘
1 個練習
Module 1 Graded Quiz30分鐘
2
完成時間為 7 小時

Module 2: Visual Features - Detection, Description and Matching

6 個視頻 (總計 44 分鐘), 5 個閱讀材料, 1 個測驗
6 個視頻
Lesson 2: Feature Descriptors6分鐘
Lesson 3 Part 1: Feature Matching7分鐘
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5分鐘
Lesson 4: Outlier Rejection8分鐘
Lesson 5: Visual Odometry9分鐘
5 個閱讀材料
Supplementary Reading: Feature Detectors and Descriptors30分鐘
Supplementary Reading: Feature Matching15分鐘
Supplementary Reading: Feature Matching15分鐘
Supplementary Reading: Outlier Rejection15分鐘
Supplementary Reading: Visual Odometry10分鐘
3
完成時間為 3 小時

Module 3: Feedforward Neural Networks

6 個視頻 (總計 58 分鐘), 6 個閱讀材料, 1 個測驗
6 個視頻
Lesson 2: Output Layers and Loss Functions10分鐘
Lesson 3: Neural Network Training with Gradient Descent10分鐘
Lesson 4: Data Splits and Neural Network Performance Evaluation8分鐘
Lesson 5: Neural Network Regularization9分鐘
Lesson 6: Convolutional Neural Networks9分鐘
6 個閱讀材料
Supplementary Reading: Feed-Forward Neural Networks15分鐘
Supplementary Reading: Output Layers and Loss Functions15分鐘
Supplementary Reading: Neural Network Training with Gradient Descent15分鐘
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10分鐘
Supplementary Reading: Neural Network Regularization15分鐘
Supplementary Reading: Convolutional Neural Networks10分鐘
1 個練習
Feed-Forward Neural Networks30分鐘
4
完成時間為 3 小時

Module 4: 2D Object Detection

4 個視頻 (總計 52 分鐘), 4 個閱讀材料, 1 個測驗
4 個視頻
Lesson 2: 2D Object detection with Convolutional Neural Networks11分鐘
Lesson 3: Training vs. Inference11分鐘
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14分鐘
4 個閱讀材料
Supplementary Reading: The Object Detection Problem15分鐘
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30分鐘
Supplementary Reading: Training vs. Inference45分鐘
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30分鐘
1 個練習
Object Detection For Self-Driving Cars30分鐘
4.6
14 個審閱Chevron Right

來自Visual Perception for Self-Driving Cars的熱門評論

創建者 RGOct 7th 2019

Many thanks for this amazing course!!!! was very hard to me but I have learned a lot!!! Thanks!!!

創建者 AAJul 18th 2019

Content is great but lack of instructor support makes the course hard to understand.

講師

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

關於 多伦多大学

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. ...

關於 自动驾驶汽车 專項課程

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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