Perception is one of the most fundamental components of autonomous vehicles. This module will introduce you to the main computer vision tasks for autonomous vehicles and the standard deep learning techniques to solve them. This course will also provide critical concepts related to image processing, a review of essential datasets, and tools used in perception for autonomous vehicles.
What You’ll Learn
- Gain a clear understanding of computers vision and the primary motivation of the field
- Understand the basic concepts of the digital imaging process.
- Know essential datasets in the field of computer vision for autonomous vehicles
- Learn the common perception tasks and how to apply deep learning architectures to solve them
- Identify the advantages and disadvantages of using RGB images and what the other alternatives
- Build and train deep neural networks for various computer vision tasks
Module Content
Pre-module reading list
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Introduction
Introduction [06:06]
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Overview [15:28]
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Main advances and challenges in the field [18:48]
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Basic Theory
The role of deep learning in computer vision [12:42]
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Camera sensors [07:36]
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Protective Geometry [13:18]
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Camera calibration [09:19]
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Perception Tasks
Image transformations [11:02]
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Object detection [22:02]
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Depth estimation [13:11]
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Optical Flow [11:01]
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Object tracking [21:56]
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Drivable surface estimation and lane detection [11:41]
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Semantic segmentation, instance segmentation, and panoptic segmentation [35:38]
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Multimodal Perception
Visual odometry [18:08]
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End of module tasks
Fusion techniques for perception tasks [15:56]
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Module exercises
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End of module reading list
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Computer Vision Algorithms Quiz
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