Parameters:
Step 1: Detection	Gaussian Mixture-Model Fitting	Gaussian Standard Deviation = 1.7 pixels
		Camera Bit Depth: 16
		Local Maxima Detection:
		Alpha-value for Comparison with Local Background = 0.05
		Do Not Check “Use Rolling Window Time-Averaging”
		Do Not Check “Use Absolute Background”
		Gaussian Fitting at Local Maxima:
		Check “Iterate to Estimate Gaussian Standard Deviation”
		Maximum Number of Iterations = 10
		Check “Do Iterative Gaussian Mixture-Model Fitting”
		Alpha values:
		Residuals = 0.05 Distance = 0.05
		Amplitude = 0.05 Final = 0
		Input and Output:
		Frames to Use = 1–100 for Population or 1–250 for Residence
Step 2: Tracking	Tracking Parameters	Parameters:
		Problem Dimensionality = 2
		Maximum Gap to Close = 5 Frames for Population or 1 Frames for Residence
		Maximum Length of Track Segments from First Step = 1 Frame
		Check “Do segment merging”
		Check “Do segment splitting”
		Do Not Check “ Plot histogram of gap lengths after gap closing”
		Check “Show calculation progress in command line”
		Do Not Check “Export tracking result to matrix format”
		Cost Functions:
		Step 1: frame-to-frame linking:
		Check “Allow direct motion position propagation”
		Check “Allow instantaneous direction reversal”
		Brownian Search Radius (in pixels):
		Lower Bound = 1
		Upper Bound = 20 for Population or 10 for Residence
		Multiplication Factor for Brownian Search Radius Calculation = 3
		Check “Use nearest neighbor distance to expand Brownian search radius”
		Number of Frames for Nearest Neighbor Distance Calculation = 20 for Population or 10 for Residence.
		Do Not Check “Plot histogram of linking distances”
		Step 2: gap closing, merging and splitting:
		Brownian + Directed motion models
		Brownian Search Radius (in pixels):
		Lower Bound = 1
		Upper Bound = 20 for Population or 10 for Residence
		Multiplication Factor for Brownian Search Radius Calculation = 3
		Check “Use nearest neighbor distance to expand Brownian search radius”
		Number of Frames for Nearest Neighbor Distance Calculation = 20 for Population and 10 for Residence
		How to expand the Brownian search radius with gap length:
		Scaling Power in Fast Expansion Phase = 0.5
		Scaling Power in slow Expansion Phase = 0.01
		Gap length to transition from Fast to Slow Expansion = 5 for Population or 1 for Residence
		Penalty for Increasing Gap Length = 1.5
		Check “In merging and splitting, consider ratio of intensities before and after merge/split:
		Ratio of Intensity: Min Allowed = 0.5 Max Allowed = 2
		Leave it Blank “Value of search Radius Lower Bound for Merging/Splitting (in pixels) ”
		Check “Allow direct motion position propagation”
		Check “Allow instantaneous direction reversal”
		Minimum Track Segment Lifetime for Classification as Linear or Random (in frames) = 5
		Multiplication Factor for Linear Search Radius Calculation = 3
		How to scale the linear motion search radius with time:
		Scaling Power in Fast Expansion Phase = 0.5
		Scaling Power in Slow Expansion Phase = 0.01
		Gap length to transition from Fast to Slow Expansion = 5 for Population or 1 for Residence
		Maximum Angle Between Linear Track Segments (in degree) = 30
		Kalman Filter Functions
		Kalman functions = Brownian + Directed motion models
		Parameters:
		Do Not Check “Initial velocity estimate (in pixels/frame) ”
		Do Not Check “Reference point for initial velocity estimate (in pixels) ”
		Check “None of the two above”
		Leave it Blank “Search Radius for first Iteration (in frames)”
Step 3: Track analysis	Motion Analysis	Motion analysis parameters:
		Problem Dimensionality = 2
		Check “Check and analyze asymmetric tracks”
		Alpha value for asymmetry determination = 0.1
		Alpha value for moment scaling spectrum analysis = 0.05
		Method for calculation the confinement radius: “mean positional standard deviation”