Computer Vision 2

SS 2014
Course Dates:
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, body pose and activity analysis. There will be regular exercises accompanying the lecture.

Further details will be announced in the first lecture on Tuesday, 15. April.


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

Course Schedule
Date Title Content Material
Introduction What is Tracking?
Exercise 0 Intro Matlab
Background Modeling MoG Background Model, Online Adaptation, Non-parametric Models
Template based Tracking LK Tracking, fast template matching, Affine LK, Line Tracking, Model based Tracking
Color based Tracking Mean-Shift Tracking, CAMshift, Comaniciu's Kernel-based Object Tracking
no class Workers' Day
Contour based Tracking Deformable Contours, Energy formulation, Greedy approach, Dynamic Programming approach, Level Sets
Tracking by Online Classification Tracking as Online Classification problem, Online Boosting, Online Feature Selection, Drift, Semi-Supervised Boosting, TLD
Exercise 1 Background Modeling, Mean-Shift Tracking
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
Bayesian Filtering II Extended Kalman Filter, Particle Filter, Case studies
Bayesian Filtering III Particle Filter Details, Sequential Importance Sampling, Reweighting, Proposal Distributions
no class (Ascension)
no class (DIES RWTH Sports Day)
Exercise 2 Bayesian Filtering
no class (Excursion week)
no class (Excursion week)
Multi-Object Tracking I Introduction, Data Association Ambiguities, Gating, NN Filter, Track Splitting Filter
no class (Corpus Christi)
no class (CANCELLED!)
no class (CANCELLED!)
Multi-Object Tracking II Multi-Hypothesis Tracking (MHT)
Multi-Object Tracking III Tracking as Linear Assignment Problem, Min-Cost Network Flow Optimization
Articulated Tracking I Body Pose Estimation as High-Dimensional Regression, Synthetic Training, Latent Variable Models, Gaussian Process Regression
Articulated Tracking II Pictorial Structures, Kinematic Tree Prior, Likelihood Models, Max-Sum Algorithm, Efficient Inference
Repetition -
Exercise 3 Multi-Object Tracking, Articulated Tracking
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