MITViterbi

plugin MITViterbi(FramePass)

Plugin Type: FramePass

An implementation of the Multi-Individual Tracking Viterbi algorithm. Runs a viterbi-like algorithm across the frames to determine the maximum scoring paths per individual, assuming an individuals can’t take a paths that would have been more likely for other individuals to have taken.

Settings

setting standard_deviation: float | Literal['auto'] = 'auto'

The standard deviation of the 2D Gaussian curve used for transition probabilities. Defaults to ‘auto’, which attempts to use an optimized value if one has been computed by a prior pass, and otherwise uses the default value of 1…

setting skeleton_weight: float = 1

A positive float, determines how much impact probabilities from skeletal transitions should have in each forward/backward step if a skeleton was created and enabled by prior passes… This is not a probability, but rather a ratio.

setting soft_domination_weight: float = 1

A positive float, determines how much impact probabilities from soft domination transitions should have in each forward/backward step if soft domination was enabled This is not a probability, but rather a ratio.

setting soft_domination_spread: float = 3

A positive float, the standard deviation of the viterbi is multiplied by this value to determine the standard deviation of the soft domination gaussian.

setting amplitude: float = 1

The max amplitude of the 2D Gaussian curve used for transition probabilities.

setting lowest_value: float = 0

The lowest value the 2D Gaussian curve used for transition probabilities can reach.

setting obscured_probability: RangedFloat[min=0.0, max=1.0] = 1e-06

A constant float between 0 and 1 that determines the prior probability of being in any hidden state cell.

setting minimum_obscured_probability: RangedFloat[min=0.0, max=1.0] = 1e-12

A constant float between 0 and 1 that sets a cutoff for obscured state probabilities.

setting enter_state_probability: RangedFloat[min=0.0, max=1.0] = 1e-12

A constant, the probability of being in the enter state.

setting enter_state_exit_probability: RangedFloat[min=0.0, max=1.0] = 0.9999

A constant, the probability of exiting the enter state. Probability of staying in the enter state is this value subtracted from 1.

setting obscured_survival_max: int = 50

An integer, the max number of points to allow to survive for each frame, if there is more than this value, the top ones are kept.

setting obscured_decay_rate: RangedFloat[min=0.0, max=1.0] = 0.99

A constant float defining the decay rate of probabilities in the occluded state.

setting gaussian_plateau: float | Literal[None] = None

A float specifying the area over which to flatten the gaussian curve should be less than the norm_dist value. If none, set to the norm_dist.

setting include_skeleton: bool = True

A boolean. If True, include skeleton information in the forward backward pass, otherwise don’t. If no skeleton has been built in a prior pass, does nothing.

setting include_soft_domination: bool = False

A boolean, if True, enable soft domination in MIT-Viterbi algorithm.Otherwise soft domination probabilities are excluded.

setting square_distances: bool = False

A boolean. If True, square distances between points before putting them through the gaussian, otherwise don’t.

setting lowest_skeleton_score: RangedFloat[min=-inf, max=0.0] = -inf

A float, the lowest allowed log-probability for the distribution of skeleton scores.This prevents the skeleton transitions from zeroing all probabilities in the viterbi.