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).
WEEK 1
2 hours to complete
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 videos (Total 18 min) 4 readings
7 hours to complete
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 videos (Total 43 min) 4 readings 2 quizzes
WEEK 2
7 hours to complete
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 videos (Total 44 min) 5 readings 1 quiz
WEEK 3
3 hours to complete
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 videos (Total 58 min) 6 readings 1 quiz
WEEK 4
3 hours to complete
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 videos (Total 52 min) 4 readings 1 quiz
Tham gia đánh giá khóa học
Nếu bạn đã học qua khóa học này thì mời bạn tham gia đóng góp ý kiến và đánh giá để cộng đồng bạn học có thêm thông tin tham khảo.
Cung cấp bởi: Coursera / University of Toronto
Thời lượng: 31 giờ
Ngôn ngữ giảng dạy: Tiếng Anh
Chi phí: Miễn phí / 0
Đối tượng: Advanced
Thông tin về nhà cung cấp
Coursera (/ kərˈsɛrə /) là một nền tảng học tập trực tuyến toàn cầu được thành lập vào năm 2012 bởi 2 giáo sư khoa học máy tính của đại học Stanford là Andrew Ng và Daphne Koller, nền tảng này cung cấp các khóa học trực tuyến (MOOC) cho cộng đồng người học online.
Coursera hợp tác với các trường đại học danh tiếng tại Bắc Mỹ và trên khắp thế giới, cùng với nhiều tổ chức khác để cung cấp các khóa học trực tuyến chất lượng, theo chuyên ngành và được cấp chứng chỉ trong nhiều lĩnh vực như kỹ thuật, khoa học dữ liệu, học máy, toán học, kinh doanh, khoa học máy tính, tiếp thị kỹ thuật số, nhân văn, y học, sinh học, khoa học xã hội , và nhiều ngành khác.