Computer Vision III: Detection, Segmentation, and Tracking
Module IN2375
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 workload | Contact hours | Credits (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
- 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.
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
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VI | 4 | Computer Vision III: Detection, Segmentation, and Tracking (IN2375) |
Araslanov, N.
Häfner, B.
Weber, S.
Responsible/Coordination: Cremers, D. Assistants: Hartwig, R.Muhle, D. |
Tue, 16:00–18:00, Interims II 004 Thu, 08:00–10:00, MI HS1 |
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.
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.
- 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.
Current exam dates
Currently TUMonline lists the following exam dates. In addition to the general information above please refer to the current information given during the course.
| Title | |||
|---|---|---|---|
| Time | Location | Info | Registration |
| Computer Vision III: Detection, Segmentation, and Tracking | |||
| 1801 |
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