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
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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
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.
- 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.
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Site Content
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Announcements
Exercises
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).
Registration
You are required to register via TUMOnline (link)!
Homeworks
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.
- To: David Tan <tanda@in.tum.de>
- Subject: [TDCV12] Exer<exercise number> - <last names of all members>
For example: [TDCV12] Exer1 - Holzer, Belangianis, Tan
- Attachment: All codes 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 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.
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
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.
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.
