| Veranstalter |
Giorgio Panin, Ph.D. |
| Modul |
IN3150 |
| Typ |
Vorlesung |
| Sprache |
Englisch |
| Semester |
WS 2008/2009 |
| ECTS |
3.0 |
| SWS |
2V |
| Hörerkreis |
Wahlfach für Studenten der Informatik (Master, Diploma) |
| Zeit & Ort |
Di 10:00 - 12:00 MI 03.07.023 |
| Schein |
Nach erfolgreicher mündlichen Prüfung |
News
Exam:
- First session: Wednesday, 18.02.09 (start 10:00), in the Glaskasten room - Informatik VI.
- Second session: Wednesday, 11.03.09 (start 10:00), probably in the Glaskasten room.
Registration: please register by writing your data on the paper, attached to the wall of Informatik VI (corridor, nearby the printer).
In order to avoid confusion, the examination ordering will be the same of the list.
Modality: oral, 30 min.
General organization: questions are taken from:
- Part I (general tools for object tracking): overall, from Lectures 1-5
- Part II (visual modalities): only from one Lecture, at choice of the student
Announcement: Hauptseminar (Sommersemester 2009) on Real-time Visual Tracking, following these Lectures.
Most of the 9 themes are still free.
Whoever interested, please visit the
webpage.
Course description
The course aims to provide a structured overview of model-based object tracking, with the purpose of estimating and following in real-time the spatial pose (rotation, translation etc.) of one or more objects, by using digital cameras and fast computer vision techniques.
The first part of the course will introduce the general tools for object tracking:
1. Pose and deformation models, and camera projection
2. Methods for pose estimation from geometric feature correspondences
3. Bayesian tracking concepts (state dynamics, measurement likelihood)
4. Bayesian filters for linear and nonlinear models, with single or multi-hypothesis state distributions
Afterwards, we will concentrate on the visual part: among the many modalities available, we will focus in particular on the following ones:
1.
Color-based: Matching color statistics, from the visible object surface to the underlying image area.
2.
Keypoint- and
Motion-based: Detection and tracking of single point features, possibly making use of image motion information (optical flow).
3.
Contour-based: Matching the object boundary line, as it deforms with the object roto-translation (also called
Active Contours).
4.
Template-based: Registration of a fully textured surface (Template) to the image gray-level intensities.
Finally, the last lecture will introduce advanced topics, concerning: multiple cameras, multiple simultaneous objects, and data fusion with multiple modalities (colors, edges, ...).
Pre-requisites
The course will also provide the following pre-requisites in a self-contained fashion (a basic knowledge would be in any case recommended):
- Basic math and algebra (nonlinear functions and derivatives, matrix computation)
- Basic geometry: 3D transformations, projective geometry, camera imaging
- Probability theory and statistics
- Basic image processing (representation, filtering etc.)
- System theory: state-space representation, dynamics, observation
Material
(for the slides, see the most recent edition of this Lectures).
Some tutorial Matlab exercises, and bibliographic references, are also available from WS06/07 (see below).
For information about our tracking project, see also the
ITrackU webpage.
Reference textbooks:
- Y. Bar-Shalom, X.-R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation, J. Wiley & Sons, 2001
Additional material:
- PoseFromPoints.pdf: Report on LSE pose estimation from point and line correspondences (Lecture 3).
- Lectures_skript.pdf: Handouts from WS06/07, with a new chapter on Lecture 6 (color-based tracking).
