Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Lab Course / Praktikum: Master Praktikum - Tracking and Detection on Computer Vision

by PD Dr. Slobodan Ilic
Team: Ivan Shugurov, Agnieszka Tomczak, Adrian Haarbach, MahdiHamad, Li Fu, You Hao
Type: Master Practical Course Module IN2106
SWS: 6
Programs: Informatics (Master), Biomedical Computing (Master)
ECTS: 10 Credits
*Lectures: Mondays 14:00-16:00h via Zoom (Check Moodle for the access code) *Tutorial(Project discussion): Thursdays 14:00-16:00h via Zoom (Check Moodle for the access code)

Further Meetings: See the schedule below. According to the pandemic situation we will partly or fully have our meetings / lectures via zoom.

This course in TUMOnline
The course will be conducted in English.


  • Praktikum lecture will take place as of Monday 02.11.2020 and Turorial(Project discussion) from Thursday 05.11.2020 via Zoom(invitation and access code are shared via Moodle)
  • Praktikum material will be shared via MOODLE
  • Application Deadline: 21-JUL-2020 Deregistration Deadline: *07-AUGUST-2020


For the application, please use the following form (Deadline : 21.07.2020): Application Form.
Please also register in the TUM matching system for the course registration. Keep in mind that your chances to be assigned to the course increase if you give it a higher rank in your choices. For further details about how the matching system works and its schedule please check this website.
We select appropriate candidates based on their background, interests, and motivation.

General Content

Tracking and Detection in Computer Vision (TDCV) stem from the TDCV course held in the previous year. The Praktikum will deal with the problems of 3D object detection and 6D pose estimation in various image modalities RGB, depth, RGB-D and point clouds with applications in Augmented Reality, Robotics, Autonomous Driving, Medical Imaging etc. It will consists of around 30% theoretical lectures explaining the key elements of the object detection and 6D object pose estimation followed by practical projects. The first part of the Praktikum will consist of a common project for all groups related to 6D tracking of the objects in sequence of RGB images. There, students will be required to implement a method for object detection and 6D pose estimation using hand-crafted features. This project introduces students to practical aspects of 6D pose parameterization, design and implementation of robust non-linear iterative least square method for 6D pose estimation and tracking. The second part of the Praktikum will be related but not limited to modern Machine Learning and Deep Learning methods for solving the problems of 3D object detection, 6D pose estimation, domain adaptation, 3D reconstruction and shape completion.

Example Applications

Example applications to follow.


  • Basic knowledge with C++ and Python programming
  • Experience with basic computer vision and machine learning concepts
  • Enthusiasm to tackle challenging problems
  • Passion to create something new with a team

Passing Requirements

  • Active participation in all mandatory workshops and meetings
  • Submission of all individual challenge solutions in time
  • Final group presentation with live prototype demonstration


Lecture Schedule and Content (Mondays 14:00-16:00h via Zoom)

Date Topic Conducted by Material/Literature
02.11 Intro & Camera Models Dr. S. Ilic Moodle
09.11 Pose Estimation from 2D-3D correspondences (DLT, PnP?), RANSAC Dr. S. Ilic  
16.11 Non-linear Optimization Dr. S. Ilic  
23.11 Rotation Parameterization, Robust Estimation Dr. S. Ilic  
30.11 Feature Detectors and Descriptors Dr. S. Ilic  
07.12. Object Detection with Deep Learning Dr. S. Ilic  
14.12 Object Detection and 6D Pose Estimation Dr. S. Ilic  
? Camera relocalization Dr. S. Ilic  

Projects Schedule (Thursdays 14:00-16:00h via Zoom)

If you have questions about a specific project, please direct them to the responsible tutor. At the due date the results of the projects will be presented by each group of students followed by the questions of the tutors. The students should be ready to run the code after their presentation as well as show parts of the code and be prepared to answer the questions about their implementation.

Date Topic Conducted by Material/Literature Sample Output
05.11. Project 1 presentation: Keypoint based object detection, 6D pose estimation and tracking Adrian Haarbach Moodle  
12.11 Q/A Adrian Haarbach    
19.11 Q/A Adrian Haarbach    
26.11 Project 2 presentation
- Traffic Sign Tracking, Multiview Localization and Mapping
- GAN-based feature space domain adaptation
- Category-Level Object Pose Estimation in Polar Mesh Object Representation
- Jointly Matching and Completion on Non-rigid Partial Data
- Category-level pose estimation with multi-view scale-aware refinement
- Paper Implementation of `DeepLS`

Adrian Haarbach
Agnieszka Tomczak
Li Fu
Hao Yu
Ivan Shugurov
Mahdi Hamad
03.12 Q/A All Tutors    
10.12 Project 1 due All Tutors    
17.12-11.02 Q/A All Tutors    
04.03 Project 2 intermediate results consultation All Tutors    
25.03 Project 2 due All Tutors    

Literature and Resources



Title: Tracking and Detection in Computer Vision
Professor: Slobodan Ilic
Tutors: Ivan Shugurov, Agnieszka Tomczak, Adrian Haarbach, MahdiHamad&#44, Li Fu, You Hao
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (Module IN4023)
Term: 2020WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 18 Mar 2021 - 13:48 - AdrianHaarbach

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