Tracking and Detection in Computer Vision - Winter Term 2011/12
Administrative Info
Lecture by Dr. Slobodan Ilic
Exercises by: Stefan Holzer, Vasilis Belagiannis, David Tan
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
Tuesday 14:30 - 16:00 MI 03.13.010
Exercises: Wednesday 10:30 - 11:30 & 13:30 - 15:00 MI 03.13.008
Consulting hours:
Lectures (Slobodan Ilic): Thursday, 10:00-11:00h MI 03.13.040
Exercises: (Stefan Holzer, Vasilis Belagiannis): Thursday, 13:30-15:00 MI 03.13.042
Requirements:
- The classes and exam are in English.
- For the intermediate 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.
- The final exam contains 100 points, you need to have 50 points to pass it
- Up to 40 bonus points can be earned from the homework projects and the intermediate exam
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Site Content
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Announcements
Results of the repetition exam can be downloaded here.
The repeat exam will take place on 29th of March at 10:00h (Seminar Room 03.13.010).
You can download the final result here (all students who failed are marked red or gray on the final grade).
Homework Results here. If there are any corrections, please e-mail to: tanda@in.tum.de
Midterm Results here.
The final exam will take place on 13.02. from 12-14:00h in 5620.01.101 (101, Interims Hörsaal 1).
The lectures start on 18th of October and the exercises the day after on 19th of October.
Access to the computerroom
Every student needs to have access to the computer in the computer room (03.13.008) before the exercises start! If you do not have access yet please sent us an email (send it to holzers at in.tum.de ) in the following format:
username (of the rechnerhalle),name,family_name,tum_email_address (that is the ...@in.tum.de email address - NO MYTUM addresses!)
Registration
You need to register via TUMOnline!
Homeworks
Depending on the complexity of the homework projects, students will from one to several weeks to solve the problems. Solutions will be checked in the following exercise session. Exact dates for every homework project will be announced on the website and said at the exercises.
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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 5 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 and pose estimation using classification approaches
and object recognition and image retrieval using bag of words approaches. In the end student will have
thorough description of the most important tracking, detection and recognition 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 and 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 majority of 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 also referred to the relevant
publications provided before the course and can use the provided lecture notes.
Lecture Schedule and Content
Lectures start on 18.10.2011 at 14:30 at
MI 03.13.010.
Exercise Schedule
Exercises start on 19.10.2011 at 13:30 at
MI 03.13.008. Here you can find a
MATLAB tutorial.
