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

Tracking and Detection in Computer Vision - Winter Term 2012/13

Administrative Info

Lecture by Dr. Slobodan Ilic
Exercises by: Stefan Holzer, Vasilis Belagiannis, David Tan, Chun-Hao Huang

Type: Lecture Module IN2210
Programs: Informatics (Bachelor, Master)
Biomedical Computing (Master)
Robotics, Cognition, Intelligence(Master)
SWS: 2+2
ECTS: 5 Credits
Course Language: English

Time, Location & Requirements

Lectures: Tuesday 14:30h - 16:00h MI 03.13.010

Exercises: Thursday 13:00h - 14:30h (Group 1) and 16:00h - 17:30h (Group 2) MI 03.13.008 and MI 03.13.010

Consultation: Tuesday 16:00h - 17:00h MI 03.13.042


  • 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.
  • Up to 40 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, including those from mid-term and homeworks, in order to pass it.
  • The bonus points from homeworks and mid-term exam are counted for the repetition exam.

Site Content



The exercise 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: Stefan Holzer an email (to: holzers at in.tum.de).


You are required to register via TUMOnline (link)!


Depending on the complexity of the homeworks, students will be given one or more weeks to solve the problems. The exact due dates for each homework will be announced on this website and will be mentioned during the exercise sessions.

All homework should be submitted through e-mail at least one hour before the first exercise session (12 noon) with the following format and information.

  1. To: David Tan <tanda@in.tum.de>
  2. Subject: [TDCV12] Exer<exercise number> - <last names of all members>
    For example: [TDCV12] Exer1 - Holzer, Belangianis, Tan
  3. Attachment: All codes 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.


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 high competitive research community with the high impact factor in computer science, but also by the emerging of numerous Vision companies turning the research ideas into the numerous commercial applications. Besides well know studies of the 3D geometry and camera models, object tracking and detection in the images and videos become one of the principal research directions of the modern Computer Vision. The main objective of this course is to provide students with gradual introduction to the modern tracking, detection and recognition techniques developed in 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 algorithm, object detection using classification approaches. In the end student will have thorough description of the most important tracking and detection techniques. They should be able to understand and implement those solutions and apply them in the reasonably complex problems. The concepts described in this course will be accompanied with the brief explanation of the necessary mathematical tools. The participants of this course will be given the necessary basis to follow vast and growing Computer Vision literature and use the acquired knowledge to solve new practical problems.

Who should attend this course?

Bachelor students at their last years and all Master students that are interested to learn about the modern and well-established concepts and algorithms related to tracking, detection and recognition of 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.


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 majority of the topics is state-of-the-art, students will be referred to the relevant publications which will be provided before the course and can use the provided lecture notes.

Lecture Schedule and Content

Lectures start on 16.10.2012 at 14:30 at MI 03.13.010.

Date Topic Conducted by Material/Literature
Tue, Oct 16 Introduction Dr. S. Ilic
Tue, Oct 23 Convolution, Non-linear filtering, Gaussian Filtering, Image Derivatives, Edge Detection Dr. S. Ilic
Tue, 30 Local invariant feature detectors: Harris corner, Harris Laplace/Affine, Hessian, Hessian-Laplace/Affine Dr. S. Ilic
Tue, Nov 6 Feature descriptors: Difference of Gaussians and SIFT, Integral images and SURF, Histogram of Oriented Gradients Dr. S. Ilic
Tue, Nov 13 Keypoint recognition: Randomized trees, FERNS, Keypoint signatures Dr. S. Ilic
Tue, Nov 20 Haar features, Ada-boost, Viola-Jones Face Detection Dr. S. Ilic
Tue, Nov 27, Dec 4 Camera models and projections, Model based tracking, Pose estimation from 2D-3D coresspondencies (DLT, P-n-P), Rotation parametrization Dr. S. Ilic
Tue, Dec 11, 18 Non-linear optimization, Robust estimators, RANSAC Dr. S. Ilic

Tue, Jan 8 Template tracking methods: Lucas-Kanade, Compositional Alg., Inverse Compositional, ESM, Linear Predictor Dr. S. Ilic
Tue, Jan 15 Mean-shift tracking Dr. S. Ilic
Tue, Jan 22 Synchronous Localization and Mapping (SLAM) Dr. S. Ilic
Tue, Jan 29 Summary Dr. S. Ilic

Exercise Schedule

Exercises start on 18.10.2012 at 13:00h(Group 1) and 16:00h(Group 2) at MI 03.13.008. Here you can find a MATLAB tutorial.

Date Topic Conducted by Material/Literature
Thu, Oct 18 Matlab Introduction/Convolution, Gaussian Filtering, Image Derivatives (due to Oct 25) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Oct 25 Median and Bilateral Filtering (due on Nov 8) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Nov 1 Allerheiligen - NO EXERCISES    
Thu, Nov 8 Harris, Harris-Laplace (due on Nov 15) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Nov 15 Sift, Dlt, Ransac, Image Stiching (due on Nov 22) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Nov 22 Ferns (due on Dec 13) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Nov 29 Mid-term  
Thu, Dec 6 Dies Academicus - NO EXERCISES    
Thu, Dec 13 Non-linear optimization (due on Dec 20) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Dec 20 Ada-boost classification (due on Jan 10) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Jan 10 Template Tracking with Linear Predictor (due on Jan 17) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Jan 17 Mean-shift Tracking (due on Jan 24) Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Jan 24 Kalman/Particle filter tracking (due on Jan 31) - Optional Stefan Holzer, Vasilis Belangianis, David Tan
Thu, Jan 31 Checking Particle Filtering Exercise Stefan Holzer, Vasilis Belangianis, David Tan  

Title: Tracking and Detection in Computer Vision
Professor: Dr. Slobodan Ilic
Tutors: Stefan Holzer, Vasilis Belagiannis, David Tan
Type: Lecture
Information: 2+2 SWS, 5 ECTS, Wahlfach im Masterstudiengang Informatik, Wahlfach im Masterstudiengang Biomedical Computing, Wahlfach im Masterstudiengang Robotics, Cognition, Intelligence
Term: 2012WiSe

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