The virtual course "Visual Perception for Autonomous Vehicles - Virtual Course - Coursera", is a course with different contents and that offers video classes of . Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Welcome to Visual Perception for Autonomous Vehicles, the third course in the Autonomous Vehicles Specialization at the University of Toronto.
This course will introduce you to the main perceptual tasks in autonomous driving, static and dynamic object detection, and examine 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 calibrations, detect, describe, and combine image features, and design your own convolutional neural networks.
You will apply these methods to visual odometry, object detection and tracking, and semantic segmentation for estimation of walkable surfaces.
These techniques represent the main basic components of the perception system of autonomous vehicles.
For the final project in this course, you will develop algorithms that identify bounding boxes for objects in the scene and define the limits of the traversable surface.
You will work with real and synthetic image data and evaluate their performance on a realistic data set.
This is an advanced course, intended for students with a background in computer vision and deep learning.
To be successful in this course, you must have programming experience in Python 3.
0 and be familiar with linear algebra (matrices, vectors, matrix multiplication, range, eigenvalues and vectors and inverses).
intended for students with a background in computer vision and deep learning.
To be successful in this course, you must have programming experience in Python 3.
0 and be familiar with linear algebra (matrices, vectors, matrix multiplication, range, eigenvalues and vectors and inverses).
intended for students with a background in computer vision and deep learning.
To be successful in this course, you must have programming experience in Python 3.
0 and be familiar with linear algebra (matrices, vectors, matrix multiplication, range, eigenvalues and vectors and inverses).
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University of Toronto
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