Rank: 602 based on 210 downloads (last 30 days) and 2 files submitted
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Eiji Ota

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MathWorks Japan
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09 Jul 2012 Screenshot Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota mathematics, image processing, particle filter, please anyone can pro... 198 49
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4.9 | 10 ratings
17 Apr 2012 Screenshot Introduction to Statistical Analysis (Japanese) Demo files used in the Japanese webinar: Introduction to Statistical Analysis Author: Eiji Ota 12 0
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21 Nov 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota

A and B are used for the calculation of log likelihood. This log likelihood is calculated under the assumption that RGB color of the object is observed with gaussian noise. But this assumption might not be true. Please think this is a toy to understand particle filter.

28 Jul 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota

Compared with particle filter, Kalman filter is relatively light algorithm.

If the object moves really really fast, fining object frame by frame might be an idea...

05 Nov 2013 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota

Would let me know the version of MATLAB you use? I've heard 64bit version of old MATLAB cannot handle video properly.

01 Apr 2013 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota

Sorry for poorly commented code...

"Xstd_rgb" means standard deviation of observation noise, which means noise you get when you observe the state of something.

"Xstd_pos" and "Xstd_vec" mean standard deviation of system noise, which describes how far actual movement of target object differs from the ideal model (in this case, linear uniform motion).

State space become of 2 componets, one is "position of particle" and another is "speed of particle".

You can define these 3 types of noise by these parameters.

11 Mar 2013 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota

I made this program based on several books written in Japanese. There's no paper related to this demo. Sorry...

You can find several papers or tutorial documents if you search internet by these keywords, "particle filter", "color tracking", "condensation".

Comments and Ratings on Eiji Ota's Files View all
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21 Nov 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota Eiji Ota

A and B are used for the calculation of log likelihood. This log likelihood is calculated under the assumption that RGB color of the object is observed with gaussian noise. But this assumption might not be true. Please think this is a toy to understand particle filter.

12 Nov 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota john

thank you for sharing code.My question is why do you use the formulaļ¼š
A = -log(sqrt(2 * pi) * Xstd_rgb);
B = - 0.5 / (Xstd_rgb.^2);
to make the likelihood,which reasons was it based on?

11 Nov 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota john

02 Nov 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota lee

05 Oct 2014 Simple Particle Filter Demo Tracking red object in a movie using particle filter. Author: Eiji Ota Azim Heidaryan

what is a reference implementation (eg, paper).

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