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Visual Perception for Self-Driving Cars

Self-Driving Cars,

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).

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Rating 4.3★ based on 18 ratings
Length 7 weeks
Effort 6 weeks of study, 5-6 hours per week
Starts Nov 7 (20 weeks ago)
Cost $49
From University of Toronto via Coursera
Instructors Steven Waslander, Jonathan Kelly
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Art & Design
Tags Computer Science Design And Product Software Development

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What people are saying

visual perception

Week 1&2 gave a good overview of visual perception and feature detection.

around 5-8 hours researching

I believe i spent around 5-8 hours researching topics on ANN and Machine learning.

dive into training neural

Though not dive into training neural net.

here would haver made

A little feedback and support here would haver made all the difference.

absolutely do not recommend

Absolutely do NOT recommend this course and will not be taking the following on.

believe i spent around

experience w/ image processing/computer

Good intro for those with not much experience w/ image processing/computer vision w.r.t.

multiples pictures generated as

The multiples pictures generated as part of your code are a great help to understand the various aspects.

spent deciphering exactly what

Way too much time was spent deciphering exactly what was to be done in the project sections.

almost every graded

I had to read forums for almost every graded assignment, that's disappointing.

detailed look under

I really loved the content of the final assignment as it provided a detailed look under the hood of a perception stack guiding you through the various stages.

basically my comments

Basically my comments from course 1 and 2 still hold.

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Visual/stock $41k

Sales/ Visual $51k

Visual Merchandiser 2 $52k

Sales/Visual Merchandiser $53k

Senior Visual $54k

Keyholder/Visual $58k

Visual Presentation Lead $69k

Senior Editor, Old Cars Weekly $78k

Computer Vision & 3D Perception $84k

Visual Program Manager $95k

Master Visual Designer $108k

Autonomous Vehicle Perception Engineer $137k

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Rating 4.3★ based on 18 ratings
Length 7 weeks
Effort 6 weeks of study, 5-6 hours per week
Starts Nov 7 (20 weeks ago)
Cost $49
From University of Toronto via Coursera
Instructors Steven Waslander, Jonathan Kelly
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Art & Design
Tags Computer Science Design And Product Software Development

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