What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Self-Driving Cars Specialization
Instructors: Steven Waslander
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(576 reviews)
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Advanced level
This is an advanced course, intended for learners with a background in computer vision and deep learning.
(576 reviews)
Recommended experience
Advanced level
This is an advanced course, intended for learners with a background in computer vision and deep learning.
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
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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).
This module introduces the main concepts from the broad and exciting 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 videos4 readings1 discussion prompt
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 videos4 readings1 assignment1 programming assignment2 ungraded labs
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 videos5 readings1 programming assignment1 ungraded lab
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 videos6 readings1 assignment
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 videos4 readings1 assignment
The second most prevalent application of deep neural networks to self-driving is semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. The main use for segmentation is to identify the drivable surface, which aids in ground plane estimation, object detection and lane boundary assessment. Segmentation labels are also being directly integrated into object detection as pixel masks, for static objects such as signs, lights and lanes, and moving objects such cars, trucks, bicycles and pedestrians.
3 videos3 readings1 assignment
The final module of this course focuses on the implementation of a collision warning system that alerts a self-driving car about the position and category of obstacles present in their lane. The project is comprised of three major segments: 1) Estimating the drivable space in 3D, 2) Semantic Lane Estimation and 3) Filter wrong output from object detection using semantic segmentation.
4 videos1 programming assignment1 discussion prompt1 ungraded lab
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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.
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Reviewed on Mar 24, 2019
Good intro for those with not much experience w/ image processing/computer vision w.r.t. autonomous driving.
Reviewed on Dec 13, 2021
Liked the overarching themes and overall content of the course. Tuning the various OpenCV algorithms was unintuitive and not discussed in the course. Discussion forums are your friend.
Reviewed on Jun 4, 2020
although I have been working with object detection and image segmentation things but still alot of learning
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