|1||10:30 - 12:00||UMIC 025||Exercise||Tutorial 2: Bayesian Filtering and Tracking as Prediction|
- We are aware that there are currently some problems with registration for this course in the new rwth online system. We are working to fix them. Please be patient.
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.
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 books:
- Computer Vision - A Modern Approach, D. Forsyth, J. Ponce, Prentice Hall, 2002
- Multiple View Geometry, R. Hartley, A. Zisserman, 2nd edition, Cambridge University Press, 2003
- 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 Online Reference Documentation
- Getting started with Matlab
- Techniques for improving performance
- A useful Matlab Quick-reference card (in German).
|Introduction||What is tracking? What is visual odometry? What is SLAM?|
|Background Modeling||Simple Background Models, Statistical Background Models, Practical Issues and Extensions|
|Template-based Tracking I||Lucas-Kanade Optical Flow, LK Feature Tracking|
|Template-based Tracking II||LK Template Tracking, Generalized LK tracking.|
|Tracking by Online Classification||Tracking by Online Classification, Boosting for Detection, Extension of boosting to Online Classification|
|Tutorial 1: Single-Object Tracking||Tutorial: Background Modelling and Generalized LK Tracking|
|Tracking by Detection||Tracking by Detection, Detectors: DPM, VeryFast, Roerei, Faster R-CNN/Mask R-CNN, YOLO...|
|-||No Class (Fachschaftsvollversammlung)|
|Bayesian Filtering I||Tracking with Linear Dynamic Models, Tracking as Inference, Prediction/Correction, Kalman Filter|
|Bayesian Filtering II||Particle Filter|
|Bayesian Filtering III||Data Association, Gating, Global NN, Linear Assignment Problem, Hungarian Algorithm|
|Tutorial 2: Bayesian Filtering and Tracking as Prediction||Tutorial: Kalman Filter, EKF, Particle Filter.|