Computer Vision 2
Semester: |
SS 2016 |
Type: |
Lecture |
Lecturer: |
|
Credits: |
Note: This page is for a course from a previous semester.
Find a list of current courses on the Teaching page.
Find a list of current courses on the Teaching page.
Type |
Date |
Room |
|---|---|---|
| Lecture | Mo, 14:15 - 15:45 | UMIC 025 |
| Lecture/Exercise | Do, 14:15 - 15:45 | UMIC 025 |
The lecture will cover advanced topics in computer vision. A particular focus will be on state-of-the-art techniques for object detection, tracking, visual odometry and SLAM. There will be regular exercises accompanying the lecture.
Literature
In the last decades, Computer Vision has evolved into a rapidly growing field with research going into so many directions that no single book can cover them all. Some basic material can be found in the following book:
- Computer Vision - A Modern Approach, D. Forsyth, J. Ponce, Prentice Hall, 2002
- An Invitation to 3D Vision, Y. Ma, S. Soatto, J. Kosecka, S. Sastry, Springer, 2003
However, a good part of the material presented in this class is the result of very recent research, so it hasn't found its way into textbooks yet. Wherever research papers are necessary for a deeper understanding, we will make them available on this web page.
Matlab Resources
- Matlab Online Reference Documentation
- Getting started with Matlab
- Techniques for improving performance
- A useful Matlab Quick-reference card (in German).
| Date | Title | Content | Material |
|---|---|---|---|
| Introduction | What is tracking? What is visual odometry? What is SLAM? | ||
| Template based Tracking | LK Tracking, fast template matching, Generalized LK | ||
| Exercise 0 | Intro Matlab | ||
| Tracking by Online Classification | Tracking as Online Classification problem, Online Boosting, Online Feature Selection, Drift, Semi-Supervised Boosting, TLD | ||
| Tracking by Detection | Tracking-by-Detection, State-of-the-Art Detectors: HOG, DPM, Viola-Jones, Integral Channel Features, VeryFast, Roerei, Hough Forests | ||
| Bayesian Filtering I | Tracking with Linear Dynamic Models, Tracking as Inference, Prediction/Correction, Kalman Filter | ||
| - | no class (Ascension) | ||
| Bayesian Filtering II | Extended Kalman Filter, Particle Filter | ||
| Bayesian Filtering III | Particle Filter Details | ||
| - | no class (Excursion week) | ||
| - | no class (Excursion week) | ||
| Exercise 1 | Generalized LK Tracking, Kalman Filter | ||
| - | no class (Corpus Christi) | ||
| Multi-Object Tracking I | Data Association, Gating, Global NN, Linear Assignment Problem, Hungarian algorithm | ||
| Multi-Object Tracking II | MHT, PDAF, JPDAF | ||
| Multi-Object Tracking III | Min-cost Network Flow Optimization, LP formulation, QBPO formulation | ||
| Exercise 2 | EKF, Particle Filter | ||
| Visual Odometry I | Introduction, Basics, Indirect Point-Based Methods | ||
| Visual Odometry II | Indirect Point-Based Methods cont., Direct Methods | ||
| Visual SLAM I | Direct Visual Odometry Methods cont., Visual SLAM: Introduction, Basics | ||
| Exercise 3 | Multi-Object Tracking, MHT | ||
| Visual SLAM II | Online SLAM Methods, Tracking-and-Mapping, EKF-SLAM, MonoSLAM | ||
| Visual SLAM III | Full SLAM Methods, SLAM Graph Optimization, Bundle Adjustment | ||
| Visual SLAM IV | Full SLAM Methods cont., Pose Graph Optimization, Data Association | ||
| Exercise 4 | Visual Odometry | ||
| Dense Reconstruction I | Dense Depth Reconstruction from Two or More Views, Depth Cameras | ||
| Dense Reconstruction II | Dense 3D Map Representations, Occupancy Grid Maps, Truncated Signed Distance Functions, Surfels | ||
| Repetition | Repetition | ||
| Exercise 5 | Visual SLAM |
