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

Tracking and Detection in Computer Vision - Winter Term 2017/18

Administrative Info

Lecture by Dr. Slobodan Ilic
Exercises by:

Type: Lecture Module IN2210
Programs: Informatics (Bachelor, Master)
Biomedical Computing (Master)
Robotics, Cognition, Intelligence(Master)
SWS: 2+4
ECTS: 7 Credits
Course Language: English

Time, Location & Requirements

Lectures: every Monday starting from ??.10.2017 at 12:15-14:00h in 00.13.009a.

Exercises: every Thursday strting from ??.10.2017 at 12:00-14:00h in 00.13.009a.

Consultation hours: Office of the responsible tutor (see names next to the exercise).


  • The classes and exams are in English.
  • For the midterm exam, nothing but your pens and your calculators are allowed.
  • For the final exam, nothing but your pens, your calculators and one DIN A4 page (handwritten, front and back side) with notes are allowed.
  • Bonus points can be earned from the homework projects and the intermediate exam.
  • The final exam brings maximally 100 points. You need to have 50 points in order to pass it. Bonus points are irrelevant for the 50-points-hurdle and are added afterwards to your final exam score.
  • There is no repetition exam.



Computer Vision, as a relatively young research area, has emerged as a key discipline in computer science. This is not only evident by a growing and highly competitive research community with a high impact factor in computer science, but also by the emergence of numerous vision companies turning research ideas into a myriad of commercial applications. Besides well-known studies of 3D geometry and camera models, object tracking and detection in images and videos becomes one of the principal research directions of modern Computer Vision. The main objective of this course is to provide students with a gradual introduction to modern tracking, detection and recognition techniques developed in the last years. The course will provide in-depth knowledge of image features, their detection and description, matching techniques, key-point recognition, basic and advanced tracking algorithms based on image features and image intensities, basics of structure from motion and bundle adjustment, synchronous localization and mapping algorithms as well as object detection using classification approaches. In the end, the students will have a thorough description of the most important tracking and detection techniques. They should be able to understand and implement those solutions and apply them in reasonably complex problems. The concepts described in this course will be accompanied with brief explanations of the necessary mathematical tools. The participants of this course will be given an important basis to follow the vast and growing Computer Vision literature and use the acquired knowledge to solve new practical problems.

Who should attend this course?

Bachelor students in their last years and all Master students that are interested in learning about the modern and well-established concepts and algorithms related to tracking, detection and recognition in Computer Vision, or desire to use those techniques in their research.

Prerequisites: Most of the knowledge required should be part of the normal background in Computer Science, undergraduate/graduate Mathematics and Geometry.


Nothing here yet.


Exercise FAQ

The exercises will be done in MATLAB and students are expected to do them on their own computer/laptop. In case you do not have your own laptop please let us know by sending an e-mail to: tdcv.tum@gmail.com.

You can also find a short MATLAB tutorial here.


You are required to register via TUMOnline (link)!


Students will be given one week to solve the problems. The exact due dates for each homework are announced next to the exercises on this website and will be mentioned during the exercise sessions. Students have to make groups of TWO students and send the names to tdcv.tum@gmail.com. After that, those who didn't create groups will be associated to groups based on a random selection and will receive a notification about this. If you are looking for a (TDCV) partner tell us and we will assign you one!

During the checking of the exercises each group will have at 15 min to be questioned to answer the questions. All members of the group will be questioned about the algorithms and their implementation, so only those who can prove that they understood both theory and practice will get the respective points. Thus, having a working exercise doesn't mean that everyone in the group is going to get full points. Please note that only exercises which work might get the maximal number of points. There will be no "debugging-correcting"-sessions for your solutions during the exercise reviews! If you have questions, ask them beforehand by either mail or in person.

All homework should be submitted through e-mail at least one hour before the exercise session with the following format and information.

  1. To: tdcv.tum@gmail.com
  2. Subject: [TDCV17] Exer<exercise number> - <group number> - <last names of all members>
    For example: [TDCV17] Exer1 - Group42 - Huang, Tan
  3. Attachment: The whole source code for this homework.
  4. Body: This should include the (a) full names and Matrikel-Nr. of each member; and, (b) reference to any code used in the homework that is not specified on the exercise sheet or by the tutors.


For some basic concepts the textbook "Computer Vision: a modern approach" by David Forsyth and Jean Ponce can be used. Also, most topics can be found in a new book of Rick Szeliski from Microsoft Research, available also on-line. However, since the majority of the topics is state-of-the-art, students will be referred to the relevant publications which will be provided during the course and can be regarded as additional lecture notes.

Lecture Schedule and Content

Date Topic Conducted by Material/Literature
- Introduction Dr. S. Ilic  
- Convolution, Non-linear filtering, Gaussian filtering, Image derivatives, Edge detection Dr. S. Ilic  
- Local invariant feature detectors: Harris corner, Harris Laplace/Affine, Hessian, Hessian-Laplace/Affine, FAST Dr. S. Ilic  
- Feature descriptors: Difference of Gaussians and SIFT, Integral images and SURF, Histogram of oriented gradients Dr. S. Ilic  
- Keypoint recognition: Randomized and regression trees, FERNS, Hough forests Dr. S. Ilic  
- Camera models, Model based tracking, Pose estimation from 2D-3D coresspondencies (DLT, P-n-P), Rotation parametrization Dr. S. Ilic  
- Midterm exam Dr. S. Ilic  
- Non-linear optimization, Robust estimators, RANSAC Dr. S. Ilic  
- Feature learning and Classification with Deep neural networks Dr. S. Ilic  
- Haar features, Ada-boost, Viola-Jones face detection Dr. S. Ilic  
- Template tracking methods: Lucas-Kanade, Compositional alg., Inverse compositional, ESM, Linear predictor Dr. S. Ilic  
- Mean-shift tracking Dr. S. Ilic  
- Template matching approaches Dr. S. Ilic  

Exercise Schedule

If you have questions about a specific exercise, please direct them to the responsible tutor.

Date Topic Conducted by Material/Literature Sample Output
- - - - -

Title: Tracking and Detection in Computer Vision
Professor: Dr. Slobodan Ilic
Type: Lecture
Information: 2 + 4; 7 ECTS
Term: 2017WiSe

Edit | Attach | Refresh | Diffs | More | Revision r1.2 - 17 Aug 2017 - 13:25 - MiraSlavcheva

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