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Tracking and Detection in Computer Vision

Modul IN2210

Dieses Modul wird durch Fakultät für Informatik bereitgestellt.

Diese Modulbeschreibung enthält neben den eigentlichen Beschreibungen der Inhalte, Lernergebnisse, Lehr- und Lernmethoden und Prüfungsformen auch Verweise auf die aktuellen Lehrveranstaltungen und Termine für die Modulprüfung in den jeweiligen Abschnitten.

Modulversion vom SS 2015 (aktuell)

Von dieser Modulbeschreibung gibt es historische Versionen. Eine Modulbeschreibung ist immer so lange gültig, bis sie von einer neuen abgelöst wird.

verfügbare Modulversionen
SS 2015WS 2011/2


IN2210 ist ein Semestermodul in Englisch auf Bachelor-Niveau und Master-Niveau das im Wintersemester angeboten wird.

Die Gültigkeit des Moduls ist bis SS 2015.

Das Modul ist Bestandteil der folgenden Kataloge in den Studienangeboten der Physik.

  • Allgemeiner Katalog der nichtphysikalischen Wahlfächer
GesamtaufwandPräsenzveranstaltungenUmfang (ECTS)
150 h 60 h 5 CP

Inhalte, Lernergebnisse und Voraussetzungen


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 probabilistic and machine learning methods for tracking and object detection. Note that every year the
course content is refreshed with new the most promising and potentially the most influential works in the
The following topics will be handled

- Introduction (overview of the course)
- Convolution and filtering:
• Basic of image formation
• Convolution and correlation
• Non-linear filtering
• Gaussian Filtering
• Image Derivatives
• Edge Detection
- Local invariant feature detectors:
• Harris corner
• Harris Laplace/Affine
• Hessian, Hessian-Laplace/Affine
- Feature descriptors:
• Difference of Gaussians and SIFT
• Integral images and SURF
• Histogram of Oriented Gradients (HOG)
- Keypoint recognition:
• Randomized trees
• Keypoint signatures
- Face detection
• Haar features
• Ada-boost
• Viola-Jones Face Detection
- Camera models and projections
• Model based tracking
• Pose estimation from 2D-3D coresspondencies (DLT, P-n-P)
• Rotation parametrization
- Non-linear optimisation
• Robust estimators
- Template tracking methods:
• Lucas-Kanade,
• Compositional Alg.
• Inverse Compositional
• Linear Predictor
- Mean-shift tracking
• mean-shift for pdf estimation
• mean-shift for segmentation
• mean-shift for object tracking
• multi-scale
- Template matching approaches
• basic correlation methods (SAD, NCC etc.)
• DOT(Dominant Orientation Template)
• LineMod (LINEarizing the memory multiMODal template matching)
- Kalman and particle filtering
• basics Kalman filer
• basics Particle filer
• applications to visual tracking
• applications to camera tracking
- Tracking with Dictionary Learning


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


Most of the knowledge required should be part of the normal background in Computer
Science, undergraduate/graduate Mathematics and Geometry.

Lehrveranstaltungen, Lern- und Lehrmethoden und Literaturhinweise

Lern- und Lehrmethoden

Lecture, combined with experimental programming assessment targeting practical implementations of the
methods explained at the lectures. The practical programming assessments are first explained at the
exercises and then given in the form of home works for students to do them in groups.


slides, blackboard, programming experiments


• lecture slides
• accompanied scientific papers and book excerpts
• Computer Vision: a modern approach" by David Forsyth and Jean Ponce
• Computer Vision: Algorithms and Applications, Rick Szeliski


Beschreibung der Prüfungs- und Studienleistungen

Type of Assessment: written exam (105 minutes)
Mid-term exam is planed after 6 lectures and it is written exam. The final exam takes place at the end of the
lectures and it also takes the form of written test. Questions allow to assess acquaintance with concepts
thought at the lectures and exercises, small calculation tasks and pseudo codes of the algorithmic solutions.
Programming tasks are not part of the examinations.
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 50 points in order to pass it. Bonus points are
irrelevant for the 50-points-hurdle and are added afterwards to your final exam score.
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