460-4131/02 – Optical Systems for Autonomous Driving (OSAJ)

Gurantor departmentDepartment of Computer ScienceCredits5
Subject guarantorIng. Radovan Fusek, Ph.D.Subject version guarantorIng. Radovan Fusek, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year1Semestersummer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
FUS032 Ing. Radovan Fusek, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

Subject aims expressed by acquired skills and competences

The course acquaints with the topics of image analysis which accompany to the autonomous driving topics. In case of completing the course, students gain an overview of modern methods of image analysis which are used in the autonomous cars.

Teaching methods

Lectures
Tutorials
Project work

Summary

The following topics are covered: Image processing methods for object detection and recognition in the area of self-driving cars. Methods for object localization in the vehicle surrounding based on the machine learning (image descriptors, convolutional neural networks, deep learning, SVM). Principles of vehicle detection, pedestrian detection, traffic sign detection, road line detection. Object recognition based on lidar and depth images.

Compulsory literature:

1. E. Sojka, Digital Image Processing, lecture notes (in Czech), VŠB-TU Ostrava,2000 (ISBN 80-7078-746-5). 2. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017 3. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010

Recommended literature:

1. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, Prentice Hall, ISBN-10: 013168728X, ISBN-13: 978-0131687288, 2007 2. Michael Beyeler: Machine Learning for OpenCV, Packt Publishing, ISBN-13: 978-1783980284, 2017

Way of continuous check of knowledge in the course of semester

The project must be worked out. The exam must be approved.

E-learning

Other requirements

No further requirements are imposed on student.

Prerequisities

Subject codeAbbreviationTitleRequirement
460-4129 ZODA Image Processing in Automobiles Recommended

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Main ideas behind object detection in images, a sliding window method. 2. Object detection methods, Haar-like features (Viola-Jones detector). 3. Local binary patterns for object detection. 4. Pedestrian and vehicle detection methods, histograms of oriented gradients. 5. Road line recognition in images. 6. Convolutional neural network. 7. Keypoint detectors and descriptors (SIFT, SURF). 8. AdaBoost and support vector machines for recognising the objects in images. 9. Traffic lights recognition in images. 10. Processing the images in IR spectrum and multispectral images. 11. Depth image processing (RealSense, Kinect). 12. LIDAR and spherical image processing. 13. Summary of lecture themes. 14. Reserve. Exercises: 1. Implementation of basic template for object detection in images. 2. Implementation of a sliding window method. 3. Preparing data for training and testing. 4. Object detection using Haar-like features. 5. Object detection using local binary patterns. 6. Object detection using histograms of oriented gradient. 7. Convolutional neural network. 8. AdaBoost and SVM for recognising the objects in images. 9. Object recognition in IR images. Image enhancements and subsequent processing. 10. Depth image processing (RealSense, Kinect). 11. LIDAR and spherical image processing. 12. Combination of detectors for autonomous cars. 13. Reserve. 14. Credit

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60  20
Mandatory attendence parzicipation: Credit: 20-40 (elaborate the tasks that form the program of exercises) Exam: 20-60 (written and oral)

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (N0716A060002) Automotive Electronic Systems P English Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner