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Instructors: Prof. Dr. Simone Frintrop; Dr. Mikko Lauri
Event type:
Project
Displayed in timetable as:
MProj - CV
Hours per week:
4
Language of instruction:
English
Min. | Max. participants:
- | 15
Comments/contents:
The project deals with computer vision methods for mobile systems, especially for autonomous mobile robots. Potential topics can be related, e.g., to active perception, object pose estimation, or deep learning methods applied to 3D data.
The project provides practical hands-on knowledge of applied computer vision systems, and experience in managing and working in a project team.
The course is split over two semesters. The second semester also includes a seminar.
Online material shared via Moodle. Students will be provided remote access to computing resources available at the department for completion of exercise tasks. Weekly online meetings with short lectures, discussions and feedback are planned. The feasibility of in-presence meetings will be determined based on the circumstances at the time of the planned event, and they can be omitted if necessary.
Didactic concept:
The project language will be English. The course relies on latest research results in computer vision and includes both reading research papers and implementing corresponding algorithmic solutions. Programming will be done primarily using Python. Additionally, frameworks such as PyTorch, OpenCV, and/or ROS can be applied.
Students will prepare project proposals which will be discussed and revised with feedback from the instructors to determine the final project topic. Organization and work on the project will be conducted independently by the students, with guidance and feedback from the instructors. Weekly meetings will be applied to track progress.
A good command of the Python programming language is required, or the willingness to self-study it. The lectures Computer Vision 1 and 2 provide sufficient background knowledge in computer vision, and it is recommended to attend both before the project. It is possible to take the lectures simultaneously with this course along with significant additional self-study. Prior knowledge of the aforementioned tools and frameworks and machine learning in general is helpful.
Literature:
Literature depends on project topics, and consists primarily of recently published research papers.
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