Administrative InfoLecture by Dr. Slobodan Ilic
Exercises by: Haowen Deng, Sergey Zakharov, Mai Bui
Type: Lecture Module IN2210
Programs: Informatics (Bachelor, Master)
Biomedical Computing (Master)
Robotics, Cognition, Intelligence(Master)
ECTS: 7 Credits
Course Language: English
Time, Location & RequirementsLectures: From 22.10.18 at 14:00-16:00h at 00.13.009A .
Exercises: From 25.10.18 at 14:00-16:00h at 00.13.009A.
OverviewComputer Vision 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. The course will address both hand crafted features and learned features with deep learning approaches. So it will be mixture of established ane emerging methods for object detection, recognition, pose estimation and tracking. 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.
AnnouncementsThe mid-term exam is fixed for: 29.11.18 from 18:30 until 20:00 in Room MW 2001 . The final exam is preliminary date is: 01.03.18 from 15:30 until 17:00 in room MW 0001. The exercise grading sessions will be held in parallel in the following rooms:
Exercise projectsThe project will be done in MATLAB, C++ and Python and students are expected to do them on their own computer/laptop. They will consist of three projects that will include one or several topics presented at the lectures. The projects will be presented to the students during the exercise sessions, where they will be instructed how to do the projects. Each project is divided into a number of exercises and each of them corresponds to an actual homework which is graded. In total all home work exercises result in a bonus for the final exam. The deadlines for the presentations and submissions of the results of the homeworks will be provided on this website. During the regular exercise classes student can work on the project and ask questions to the tutors. The exact due dates for each project are announced next to the exercise description on this website and will be defined beforehand (check exercise section). Students have to make groups of up to FOUR students and send the names to firstname.lastname@example.org. 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 the presentation of the results for each project each group will have 5 min to present their solution in terms of slides, and also run the code live. This will be followed by questions from the tutors (~ 5min). 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 project results presentation! If you have questions, ask them beforehand during the exercise sessions. All homework should be submitted through e-mail before the exercise session with the following format and information.
RegistrationYou are required to register via TUMOnline!
|Title:||Tracking and Detection in Computer Vision|
|Professor:||Dr. Slobodan Ilic|
|Tutors:||Tolga Birdal, Haowen Deng, Sergey Zakharov, Mai Bui|
|Information:||2 + 4; 7 ECTS|