U of T - Visual Perception for Self-Driving Cars
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- Public/Government Institute
Visual Perception for Self-Driving Cars at Coursera Overview
Duration | 31 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Visual Perception for Self-Driving Cars at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 4 in the Self-Driving Cars Specialization
- 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 Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Visual Perception for Self-Driving Cars at Coursera Course details
- Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto?s Self-Driving Cars Specialization.
- This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks. You'll apply these methods to visual odometry, object detection and tracking, and semantic segmentation for drivable surface estimation. These techniques represent the main building blocks of the perception system for self-driving cars.
- For the final project in this course, you will develop algorithms that identify bounding boxes for objects in the scene, and define the boundaries of the drivable surface. You'll work with synthetic and real image data, and evaluate your performance on a realistic dataset.
- This is an advanced course, intended for learners with a background in computer vision and deep learning. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses).
Visual Perception for Self-Driving Cars at Coursera Curriculum
Welcome to Course 3: Visual Perception for Self-Driving Cars
Welcome to the Self-Driving Cars Specialization!
Welcome to the course
Meet the Instructor, Steven Waslander
Meet the Instructor, Jonathan Kelly
Course Prerequisites
How to Use Discussion Forums
How to Use Supplementary Readings in This Course
Recommended Textbooks
Lesson 1 Part 1: The Camera Sensor
Lesson 1 Part 2: Camera Projective Geometry
Lesson 2: Camera Calibration
Lesson 3 Part 1: Visual Depth Perception - Stereopsis
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity
Lesson 4: Image Filtering
Supplementary Reading: The Camera Sensor
Supplementary Reading: Camera Calibration
Supplementary Reading: Visual Depth Perception
Supplementary Reading: Image Filtering
Module 1 Graded Quiz
Module 2: Visual Features - Detection, Description and Matching
Lesson 1: Introduction to Image features and Feature Detectors
Lesson 2: Feature Descriptors
Lesson 3 Part 1: Feature Matching
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching
Lesson 4: Outlier Rejection
Lesson 5: Visual Odometry
Supplementary Reading: Feature Detectors and Descriptors
Supplementary Reading: Feature Matching
Supplementary Reading: Feature Matching
Supplementary Reading: Outlier Rejection
Supplementary Reading: Visual Odometry
Module 3: Feedforward Neural Networks
Lesson 1: Feed Forward Neural Networks
Lesson 2: Output Layers and Loss Functions
Lesson 3: Neural Network Training with Gradient Descent
Lesson 4: Data Splits and Neural Network Performance Evaluation
Lesson 5: Neural Network Regularization
Lesson 6: Convolutional Neural Networks
Supplementary Reading: Feed-Forward Neural Networks
Supplementary Reading: Output Layers and Loss Functions
Supplementary Reading: Neural Network Training with Gradient Descent
Supplementary Reading: Data Splits and Neural Network Performance Evaluation
Supplementary Reading: Neural Network Regularization
Supplementary Reading: Convolutional Neural Networks
Feed-Forward Neural Networks
Module 4: 2D Object Detection
Lesson 1: The Object Detection Problem
Lesson 2: 2D Object detection with Convolutional Neural Networks
Lesson 3: Training vs. Inference
Lesson 4: Using 2D Object Detectors for Self-Driving Cars
Supplementary Reading: The Object Detection Problem
Supplementary Reading: 2D Object detection with Convolutional Neural Networks
Supplementary Reading: Training vs. Inference
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars
Object Detection For Self-Driving Cars
Module 5: Semantic Segmentation
Lesson 1: The Semantic Segmentation Problem
Lesson 2: ConvNets for Semantic Segmentation
Lesson 3: Semantic Segmentation for Road Scene Understanding
Supplementary Reading: The Semantic Segmentation Problem
Supplementary Reading: ConvNets for Semantic Segmentation
Supplementary Reading: Semantic Segmentation for Road Scene Understanding
Semantic Segmentation For Self-Driving Cars
Module 6: Putting it together - Perception of dynamic objects in the drivable region
Project Overview: Using CARLA for object detection and segmentation
Final Project Hints
Final Project Solution [LOCKED]
Congratulations for completing the course!
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