INtroduction Kernel density I Kernel choices Peak finding I Mean-shift Cam-shift First we need to understand the Probability density Function PDF We use Kernel density estimation to find PDF Obtain the probability function from samples Camshift v 0.a 6
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift First we need to understand the Probability Density Function PDF We use Kernel density estimation to find PDF Obtain the probability function from samples Camshift v.0.a 6
Introduction (Kernel density Kernel choices I Peak finding I Mean-shift |Cam-shift Motivation for Kernel density estimation to find pdf The formula(parametric form) of the PDf (probability density function is difficult to find Use sampling method to estimate the p.D.f That means: Gaussian(a parametric form with mean, standard deviation etc. is easy to use) but it is too simple to model real life problems PDF(X N/iToo simple to model o onaL HR real life problems KN(x)=ceiiA X An irregular shape pdf, the distribution Gaussian distribution Is difficult to model using parameters camshift v .a --use non-parametric methods instead
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift Motivation for Kernel density estimation to find PDF • The formula (parametric form) of the PDF (probability density function) is difficult to find. • Use sampling method to estimate the P.D.F. • That means: Gaussian ( a parametric form with mean , standard deviation etc., is easy to use), but it is too simple to model real life problems. 2 || || 2 1 ( ) x N K x c e − = Camshift v.0.a 7 Gaussian distribution An irregular shape PDF, the distribution Is difficult to model using parameters --use non-parametric methods instead PDF(x) 0 x Too simple to model real life problems
IntroductionKKernel density Kernel choices I Peak finding IMean-shiftICam-shift Example Outbreak of flu in a year How do you model this pdf? CUHK Clinic Patients Number 100+ Per day 3 9 12 month Camshift v 0.a 8
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift Example • Outbreak of flu in a year • How do you model this PDF? Camshift v.0.a 8 month CUHK Clinic Patients Number Per day 3 6 9 12 100
Introduction KErnel density Kernel choices I Peak finding I Mean-shift I Cam-shift Kernel density estimation KDE Demo mei Density Estm Dataset 0waBa们a钟(动 https://courses.cs.ut.ee/demos/kernel-Density-estimation/ https:/en.wikipedia.org/wiki/kerneldensityestimation Camshift v 0.a 9
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift Kernel density estimation KDE Demo • Camshift v.0.a 9 https://courses.cs.ut.ee/demos/kernel-density-estimation/ https://en.wikipedia.org/wiki/Kernel_density_estimation
Introduction KErnel density Kernel choices I Peak finding I Mean-shift I Cam-shift kernel density distribution function K is a function To be explained(see slide 19) The general form of a kernel x-xi density distribution function x)=∑k The Kernel (k) has many n=number of samples choices h= window radius Epanechnikov d= dimension Uniform get position Normal ( gaussian) x= samples C= normalization constant Camshift v 0.a
Introduction | Kernel density | Kernel choices | Peak finding | Mean-shift | Cam-shift kernel density distribution function • The general form of a kernel density distribution function • The Kernel (K) has many choices – Epanechnikov – Uniform – Normal (Gaussian) C normalization constant samples target position dimension window radius number of samples ( ) ˆ 1 = = = = = = − = = i n i i h d x xd h n h x x K nhC f x Camshift v.0.a 10 K is a function: To be explained (see slide19)