Computer Vision 2


WS 2018





Note: This page is for a course from a previous semester.
Find a list of current courses on the Teaching page.


  • 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.

Lecture Description

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 Resources

Course Schedule
Date Title Content Material
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)
Tracking with Linear Dynamic Models Tracking with Linear Dynamic Models, Tracking as Inference, Prediction/Correction, Kalman Filter
Beyond Kalman Filters Kalman Filter, EKF, Particle Filter
- No Class
Tutorial 2: Bayesian Filtering and Tracking as Prediction Tutorial: Kalman Filter, EKF, Particle Filter.
Particle Filters Particle Filter details
Multi-Object Tracking I Data Association, Gating, Global NN, Linear Assignment Problem, Hungarian Algorithm
Multi-Object Tracking II MHT, PDAF, JPDAF
Tutorial 2: Multi-Object Tracking and MHT Tutorial: Multi-Object Tracking, MHT
Visual Odometry I Visual Odometry
Visual Odometry II Visual Odometry
Visual Odometry III Visual Odometry
Visual SLAM I Visual SLAM
Visual SLAM II Visual SLAM
CNNs for Video I CNNs for Video
CNNs for Video II CNNs for Video
CNNs for Video III CNNs for Video
Repetition Repetition
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