| Course Unit Code | 460-4131/01 |
|---|
| Number of ECTS Credits Allocated | 5 ECTS credits |
|---|
| Type of Course Unit * | Compulsory |
|---|
| Level of Course Unit * | Second Cycle |
|---|
| Year of Study * | First Year |
|---|
| Semester when the Course Unit is delivered | Summer Semester |
|---|
| Mode of Delivery | Face-to-face |
|---|
| Language of Instruction | Czech |
|---|
| Prerequisites and Co-Requisites | |
|---|
| Prerequisities | Course Unit Code | Course Unit Title |
|---|
| 460-4129 | Image Processing in Automobiles |
| Name of Lecturer(s) | Personal ID | Name |
|---|
| FUS032 | Ing. Radovan Fusek, Ph.D. |
| 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. |
| Learning Outcomes of the Course Unit |
|---|
| 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. |
| Course Contents |
|---|
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.
|
| Recommended or Required Reading |
|---|
| Required Reading: |
|---|
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
|
1. E. Sojka, Digitální zpracování a analýza obrazů, učební texty, 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 Reading: |
|---|
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 |
1. E. Sojka, J. Gaura, and M. Krumnikl, Matematické základy digitálního zpracování obrazu. Ostrava, Plzeň: VŠB-TU Ostrava (Fakulta elektrotechniky a informatiky), Západočeská univerzita v Plzni, 2. vydání ed., 2011.
2. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, Prentice Hall, ISBN-10: 013168728X, ISBN-13: 978-0131687288, 2007
3. Michael Beyeler: Machine Learning for OpenCV, Packt Publishing, ISBN-13: 978-1783980284, 2017 |
| Planned learning activities and teaching methods |
|---|
| Lectures, Tutorials, Project work |
| Assesment methods and criteria |
|---|
| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
|---|
| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 | 20 |
| Examination | Examination | 60 | 20 |