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Computer Vision for Video Analysis, 6 credits
Datorseende för videoanalys, 6 hp
TSBB34
Main field of study
Computer Science and Engineering Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Bastian WandtDirector of studies or equivalent
Lasse AlfredssonEducation components
Preliminary scheduled hours: 100 hRecommended self-study hours: 60 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
The course syllabus is preliminary
Main field of study
Computer Science and Engineering, Electrical EngineeringCourse level
Second cycleAdvancement level
A1NSpecific information
The course can not be included in a degree together with TSBB15.
Course offered for
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Computer Science and Engineering
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Biomedical Engineering
- Master's Programme in Data Science and Information Engineering
Prerequisites
Probability theory, estimation theory, the least squares method, partial differential equations, 1D & 2D linear system theory (deterministic and stochastic).
Basic image processing: thresholding, segmentation, edge detection.
Use of Python.
As half the course is project work, experience with programming is also recommended.
Intended learning outcomes
The course gives knowledge on the algorithms and estimation problems used to extract information from videos or image sequences. This includes both the mathematics used, and how these are put into practice in algorithm implementation.
After the course, the students should be able to:
Goal 1: explain and use algorithms for tracking of regions in image sequences
Goal 2: explain and use algorithms for estimating optical flow
Goal 3: explain and integrate components for object tracking in image sequences
Goal 4: explain and integrate components for debugging, visualization, and performance evaluation
Course content
This course teaches methodology related to the goals listed above, with focus on the following:
- Local features and the structure tensor
- Motion estimation and optical flow
- Clustering and background modeling
- Tracking of regions and objects
- Discriminative correlation filters
- Camera surveillance and its ethical/societal aspects
The contents are introduced in a lecture series, and are then put to use in computer exercises and a programming project.
Teaching and working methods
The course consists of a lecture series, lessons, two computer exercises, and a programming project conducted in groups of students. The computer exercises introduce key components of the project and require programming.
Examination
| PRA2 | Project Work | 3 credits | U, 3, 4, 5 |
| LAB1 | Laboratory Work | 3 credits | U, 3, 4, 5 |
Attendance is mandatory at the computer exercises, the project presentation seminar, and at the lecture where the project starts.
Goals 1-2 are tested during the computer exercises and Goals 3-4 during the project.
For grade 3, a pass on the project and the computer exercises are required. Demonstrating higher abilities to explain and use methods in the projects or computer exercises results in grade 4, demonstrating higher abilities to explain and use methods in the projects and computer exercises results in grade 5.
Presentation of details of the assessment criteria can be found on the course web page.
Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses:
3D Computer Vision, Images and Graphics, Project Course CDIO, Machine learning for computer vision, Thesis
About teaching and examination language
The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows:
- If teaching language is “Swedish”, the course as a whole could be given in Swedish, or partly in English. Examination language is Swedish, but parts of the examination can be in English.
- If teaching language is “English”, the course as a whole is taught in English. Examination language is English.
- If teaching language is “Swedish/English”, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English depending on teaching language.
Other
The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.
The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point.
The course is campus-based at the location specified for the course, unless otherwise stated under “Teaching and working methods”. Please note, in a campus-based course occasional remote sessions could be included.
Department
Institutionen för systemteknikCourse literature
Books
- Michael Felsberg, (2022) Advanced Methods and Deep Learning in Computer Vision Academic Press
- Richard Szeliski, (2022) Computer Vision: Algorithms and Applications 2 Springer
Other
A selection of papers specified at the course website.
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| PRA2 | Project Work | 3 credits | U, 3, 4, 5 |
| LAB1 | Laboratory Work | 3 credits | U, 3, 4, 5 |
Attendance is mandatory at the computer exercises, the project presentation seminar, and at the lecture where the project starts.
Goals 1-2 are tested during the computer exercises and Goals 3-4 during the project.
For grade 3, a pass on the project and the computer exercises are required. Demonstrating higher abilities to explain and use methods in the projects or computer exercises results in grade 4, demonstrating higher abilities to explain and use methods in the projects and computer exercises results in grade 5.
Presentation of details of the assessment criteria can be found on the course web page.
Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.
Books
Michael Felsberg, (2022) Advanced Methods and Deep Learning in Computer Vision Academic PressMichael Felsberg Visual tracking: Tracking in scenes containing multiple moving objects
Richard Szeliski, (2022) Computer Vision: Algorithms and Applications 2 Springer
Other
A selection of papers specified at the course website.
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
|
|
X
|
LAB1
|
Linear algebra, Calculus |
|
| 1.2 Fundamental engineering knowledge (G1X level) |
X
|
X
|
X
|
LAB1
|
least squares model fitting, Signal processing |
|
| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
X
|
X
|
X
|
LAB1
|
partial differential equations, robust model fitting |
|
| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
X
|
X
|
X
|
PRA2
|
||
| 1.5 Insight into current research and development work |
X
|
X
|
X
|
PRA2
|
The course uses recent techniques, and we have guest lectures from the industry |
|
| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
X
|
PRA2
LAB1
|
Planning and execution of a group project |
|
| 2.2 Experimentation, investigation, and knowledge discovery |
|
|
X
|
PRA2
|
Tests and comparisons of alternative approaches |
|
| 2.3 System thinking |
|
|
X
|
PRA2
LAB1
|
Software design and integration |
|
| 2.4 Attitudes, thought, and learning |
|
|
X
|
PRA2
|
Personal responsibility for specific project parts |
|
| 2.5 Ethics, equity, and other responsibilities |
|
|
X
|
PRA2
|
Planning and execution of a group project |
|
| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
|
X
|
PRA2
|
Planning and execution of a group project |
|
| 3.2 Communications |
|
|
X
|
PRA2
|
Written and oral presentation of project results, and feedback on an other group's work |
|
| 3.3 Communication in foreign languages |
|
|
X
|
PRA2
LAB1
|
When the course is given in English |
|
| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
|
|
|
|||
| 4.2 Enterprise and business context |
X
|
|
|
We have guest lectures from the industry |
||
| 4.3 Conceiving, system engineering and management |
X
|
|
|
A project plan is written in the project |
||
| 4.4 Designing |
X
|
|
|
Project work |
||
| 4.5 Implementing |
X
|
|
|
Project work |
||
| 4.6 Operating |
|
|
|
|||
| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
|
|
|
|||
| 5.2 Economic conditions for knowledge development |
|
|
|
|||
| 5.3 Identification of needs, structuring and planning of research or development projects |
X
|
|
|
A project plan is used in the project |
||
| 5.4 Execution of research or development projects |
X
|
|
|
The course has a programming project |
||
| 5.5 Presentation and evaluation of research or development projects |
X
|
|
|
Rapportskrivning enligt uppställd mall. Muntlig redovisning. Opposition |
||
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