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Computer Vision III: Detection, Segmentation, and Tracking

Module IN2375

This Module is offered by TUM Department of Informatics.

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Basic Information

IN2375 is a semester module in English language at Master’s level which is offered in winter semester.

This Module is included in the following catalogues within the study programs in physics.

  • Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
Total workloadContact hoursCredits (ECTS)
180 h 60 h 6 CP

Content, Learning Outcome and Preconditions

Content

- Proposal-based object detection (Faster-RCNN)
- One-stage detectors (YOLO, SSD, RetinaNet)
- Point-based detection
- Instance segmentation (Mask-RCNN)
- Semantic segmentation
- Panoptic segmentation
- Video object segmentation (OSVOS)
- Visual object tracking
- Multiple object tracking
- Graph neural networks for object tracking
- 3D object tracking
- Trajectory prediction

Learning Outcome

Upon completion of this module, students will have acquired theoretical concepts behind the object detection, segmentation and tracking algorithms. They will be able to implement their own solution to solve these practical real-world problems with deep learning.

Preconditions

IN2346 Introduction to Deep Learning
MA0902 Analysis für Informatiker
MA0901 Lineare Algebra für Informatiker

Knowledge of Python and Pytorch is a must to complete the course practical assignments.

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

TypeSWSTitleLecturer(s)DatesLinks
VI 4 Computer Vision III: Detection, Segmentation, and Tracking (IN2375) Dendorfer, P. Leal-Taixe, L. Toker, A.
Assistants: Elezi, I.
Thu, 14:00–16:00, virtuell
Tue, 10:00–12:00, virtuell
eLearning

Learning and Teaching Methods

The lectures will provide extensive theoretical aspects of neural networks and in particular deep learning architectures for the problems of object detection, segmentation and tracking.
The practical sessions will allow the students to get familiar with state-of-the-art algorithms (implemented in PyTorch) and the tricks of trainings them for the aforementioned Computer Vision tasks.

Media

Projector, blackboard, PC

Literature

Links will be provided during the lecture

Module Exam

Description of exams and course work

The exam takes the form of a written test. Questions allow to assess acquaintance with concepts and algorithms of deep learning for object detection, segmentation (both object as well as semantic) and multiple object tracking. Students demonstrate the ability to design, train, and optimize neural network architectures to solve these basic computer vision problems.

- Written test of 90 minutes at the end of the course.
- The students will be awarded a bonus in case they successfully complete all practical assignments. Their progress and/or final results will be presented in the form of a poster or an oral presentation.

Exam Repetition

The exam may be repeated at the end of the semester. There is a possibility to take the exam in the following semester.

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