Graduate study programme

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Digital Image and Video Processing for Autonomous Vehicles DA3-02

ECTS 5 | P 30 | A 0 | L 15 | K 15 | ISVU 177875 | Academic year: 2019./2020.

Course groups

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Course lecturers

RIMAC-DRLJE SNJEŽANA, Lecturer
VRANJEŠ MARIO, Lecturer
VRANJEŠ DENIS, Associate
CUPEC ROBERT, Lecturer
VAJAK DENIS, Associate

Goals

Introduce students to ADAS systems features. Introduce students to features of digital images and video signals as well as with the camera system in vehicles. Enable students to apply and develop advanced algorithms for processing of digital images and video signals, with an emphasis on algorithms for usage in autonomous vehicles.

Conditions for enrollment

Requirements met for enrolling in the study programme

Course description

Introduction to ADAS (Advanced Driver Assistance Systems). Characteristics of digital image and video. Advanced algorithms for real-time and video processing used in autonomous vehicles. Image processing: compression processes, image enhancement methods, edge detection, object detection, classification and recognition, scene segmentation, optical character recognition. Video – video standards, 3D scene reconstruction, time tracking of objects, stereovision, pedestrians detection using cameras. Camera systems in vehicles. Camera mirror replacement.

Student requirements

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Monitoring of students

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Obligatory literature

1. 1 H. Winner, S.Hakuli, F. Lotz, C.Singer Handbook of Driver Assistance Systems Springer 2016.

2. 2 A. Terzis Handbook of Camera Monitor Systems The Automotive Mirror-Replacement Technology based on ISO 16505 Springer 2016.


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Recommended additional literature

1. 1 J. Ohm Multimedia Signal Coding and Transmission (Signals and Communication technology) Berlin Heidelberg, Springer, 2015.

2. 2 R. C. G. Gonzalez; R. E Woods Digital Image Processing New Jersey: Pearson Education, 2008.

Course assessment

Conducting university questionnaires on teachers (student-teacher relationship, transparency of assessment criteria, motivation for teaching, teaching clarity, etc.). Conducting Faculty surveys on courses (upon passing the exam, student self-assessment of the adopted learning outcomes and student workload in relation to the number of ECTS credits allocated to activities and courses as a whole).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. evaluate different ADAS systems

2. compare image and video processing algorithms for autonomous vehicles

3. apply advanced image and video processing algorithms in real time

4. evaluate the camera systems for autonomous driving

5. apply processing algorithms on images acquired by the camera mirror replacement system

6. develop a prototype of a custom real-time image and video processing algorithm for application in autonomous vehicles



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