Currently viewing the tag: "Monte Carlo"

Tracking tutorial

On August 24, 2011 · Leave a Comment

Together with Prof. Andrea Cavallaro I will be giving a tutorial on multi-target tracking at the International Conference on Image Processing (ICIP) in Brussels (Tutorial day: 11 September 2011). After a brief introduction on tracking basics and object detection techniques, the tutorial will focus on data association and spatio-temporal filtering [...]

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Particle Filter and Mean Shift tracking

On June 21, 2011 · 2 Comments

Particle Filter and Mean Shift tracking

 

Particle filter is the name given to the sample-based (i.e., Montecarlo) approximation of the Bayes recursion. The algorithm is a popular substitute for the Kalman filter in presence of non-Gaussianity of the noise statistics and non-linearity  of the relationships [...]

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Monte Carlo approximation of the PHD

My research and Random Finite Sets

On March 8, 2011 · Leave a Comment

I have created a new section of the website that I will populate with information on my past and present research. I have started  with a page on the use of Random Finite Sets to track multiple-targets in video sequences. I have also added a couple of interesting videos: one shows an example of [...]

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Tracking with Random Finite Sets

On March 3, 2011 · Leave a Comment

Tracking with Random Finite Sets

 

Many tracking algorithms use the Bayes recursion to propagate uncertain estimates based on uncertain observation over time. This is a well established probabilistic framework for single-target tracking. A problem arises if we want to extend the Bayes framework to multiple objects and we are required to estimate the number [...]

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