Models¶

Face tracking model¶

class pftracker.modules.models.dlgModel.dlg(F, muW, SigmaW)[source]¶

Propagate particles from time k-1 to k using a Discrete-time Linear and gaussian model in the prediction step of particle filters.

Parameters

F (array) – State transition matrix with (state_vector_size, state_vector_size) dimension
muW (array) – Noise-system mean vector with (state_vector_size,1) dimension
SigmaW (array) – Noise-system covariance matrix with (state_vector_size, state_vector_size) dimension

move_particles(xk_1, N)[source]¶

Move particles from time k-1 to time k.

Parameters

xk_1 (array) – particles in the previous state, with (state_vector_size,N) dimension.
N (int) – number of samples

Returns

2-element tuple containing

xk (array): particles in the actual state with (state_vector_size,N) dimension.
muk (array): characterization of x_{k}|x_{k-1} where particles are moved particles from x_{k-1} to x_{k} without include the process noise.