This course covers Computer Vision and Perception for Self-Driving Cars, focusing on tasks required by a Self-Driving Car Perception unit. Students will learn about Fully Convolutional Networks for road segmentation, YOLO for 2D object detection, Deep SORT for object tracking, KITTI 3D data visualization using homogenous transformations, Multi Task Attention Network for multi-task learning, and SFA 3D for 3D object detection. The course includes hands-on experience with datasets, notebooks, code, and relevant papers and blogs. The intended audience for this course is individuals interested in deep learning applications for autonomous vehicles and computer vision enthusiasts.
Overview
Syllabus
) Introduction.
) Fully Convolutional Network | Road Segmentation.
) YOLO | 2D Object Detection.
) Deep SORT | Object Tracking.
) KITTI 3D Data Visualization | Homogenous Transformations.
) Multi Task Attention Network (MTAN) | Multi Task Learning.
) SFA 3D | 3D Object Detection.
) UNetXST | Camera to Bird's Eye View.
Taught by
freeCodeCamp.org
Related Courses
-
Visual Perception for Self-Driving Cars
-
Autonomous Cars: Deep Learning and Computer Vision in Python
-
The Complete Self-Driving Car Course - Applied Deep Learning
-
Data Science: Deep Learning Project for Self Driving Cars
-
Machine Learning & Self-Driving Cars: Bootcamp with Python
-
Python for Computer Vision with OpenCV and Deep Learning
