Tracking and Detection in Computer Vision - Winter Term 2013/14
Administrative Info
Lecture by Dr. Slobodan Ilic
Exercises by: Vasilis Belagiannis, David Tan, Paul Huang, Wadim Kehl
Type: Lecture Module IN2210
Programs: Informatics (Bachelor, Master)
Biomedical Computing (Master)
Robotics, Cognition, Intelligence(Master)
SWS: 2+2
ECTS: 5 Credits
Course Language: English
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Time, Location & Requirements
Lectures: Monday 12:20h - 14:00h Media Raum MI 00.13.009A
Exercises: Thursday 12:00h - 14:00h Media Raum MI 00.13.009A
Consultation hours: Monday 14:00h - 16:00h Office of the responsible tutor(see names next to the exercise). To get an a slot for consultation send an e-mail to tdcv.tum@gmail.com latest on Sunday before the consultation hours.
Requirements:
- 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 40 points in order to pass it. Bonus points are irrelevant for the 40-points-hurdle and are added afterwards to your final exam score.
- There is no repetition exam.
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Site Content
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Announcements
FINAL GRADES: link
The exam inspection (possibility to look into your exam correction) will take place on Monday, 24.03., at 11:00-12:00 in the CAMP seminar room MI 03.13.010.
For all questions please send e-mails to: tdcv.tum@gmail.com. E-mails sent directly to tutors and professors will not be answered.
Exercises
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.
Registration
You are required to register via TUMOnline (link)!
Homeworks
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 four and send the names to tdcv.tum@gmail.com until 25th of October. 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.
During the checking of the exercises each group will have 5 minutes to answer 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 (11:00AM) with the following format and information.
- To: tdcv.tum@gmail.com
- Subject: [TDCV13] Exer<exercise number> - <last names of all members> - GROUP_{number of the group};
For example: [TDCV13] Exer1 - Belagiannis, Huang, Kehl, Tan - Group10
- Attachment: The whole source code for this homework.
- 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.
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Overview
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.
Textbooks
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
Exercise Schedule
