trackOSPAMetric for my data
Hello. I want to use trackOSPAMetric to evaluate tracking result with artificial data. but it does not match my data. I attached the code and the error. any ideas? thank you very much for your time and attention.
clc; clear; close;
% 2DPF+3DOSPA
% Parameters
num_trajectories = 4;
num_paths = 2;
points_per_trajectory = 100;
num_clusters = 60;
% Define base paths as nearly horizontal lines
base_paths = { [linspace(0, 10, points_per_trajectory)’, 2 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 4 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 6 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 8 * ones(points_per_trajectory, 1)] };
% Initialize trajectories
trajectories = cell(1, num_trajectories);
% Generate trajectories with random deviations from base paths
for ii = 1:num_trajectories
path = base_paths{mod(ii, num_paths) + 1};
deviation = [0.01 * randn(points_per_trajectory, 1), …
0.01 * randn(points_per_trajectory, 1)];
trajectories{ii} = path + deviation;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Select trajectory for tracking
selected_traj = trajectories{1};
numParticles = 1000;
stateDimension = 4;
init_state = [selected_traj(1, 1); 0;…
selected_traj(1, 2); 0];
pf = trackingPF(@constvel, @cvmeas, init_state, …
‘StateCovariance’, 0.5 * eye(stateDimension), …
‘NumParticles’, numParticles, …
‘HasAdditiveProcessNoise’, true);
% Particle filter tracking
estimated_positions_pf = …
zeros(size(selected_traj, 1), stateDimension);
pf_tot = cell(size(selected_traj, 1),1);
estimated_positions_pf(1, 🙂 = init_state’;
pf_no_km = clone(pf);
for t = 2:size(selected_traj, 1)
[xPred_pf, PPred_pf] = predict(pf_no_km, 1);
observation = …
[selected_traj(t, 1); selected_traj(t, 2); 0];
[xCorr_km, Pcorr_km] = correct(pf, observation);
[xCorr_pf, Pcorr_pf] = correct(pf_no_km, observation);
estimated_positions_pf(t, 🙂 = xCorr_pf;
pf_tot{t} = Pcorr_pf;
end
% Plot tracking results
figure (101);
plot(selected_traj(:, 1), selected_traj(:, 2), ‘g’, …
‘DisplayName’, ‘True Trajectory’);
hold on;
plot(estimated_positions_pf(:, 1), …
estimated_positions_pf(:, 3), ‘bo-.’, …
‘DisplayName’, ‘Estimated (No KM)’);
title(‘Tracking Map’);
legend;
xlabel(‘X’);
ylabel(‘Y’);
hold off;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% OSPA metric computation
truths_mine = …
[selected_traj zeros(size(selected_traj,1),1)];
%%
% Construct the truth and tracks structs
num_timesteps = size(selected_traj, 1);
truths_cell = cell(1,num_timesteps);
tracks_PF_cell = cell(1,num_timesteps);
% Compute velocities for truth data
velocities_truth = diff(truths_mine); % Approximate velocity (forward difference)
velocities_truth = [0 0 0;velocities_truth];
for t = 1:num_timesteps
% Truth struct
truth_struct = struct();
truth_struct.PlatformID = 1;
truth_struct.Time = t; % Time step
truth_struct.Position = …
[selected_traj(t, 1); selected_traj(t, 2);0]’; % Position [x; y]
% if t < num_timesteps
truth_struct.Velocity = velocities_truth(t, :); % Velocity [vx; vy]
truths_cell{t} = truth_struct;
% Tracks struct
tracks_struct_PF = struct();
tracks_struct_PF.UpdateTime = t; % Time step
tracks_struct_PF.State = …
[estimated_positions_pf(t, :),0,0]’;%; estimated_positions_pf(t, 3);0]; % Estimated position [x; y]
tracks_struct_PF.TrackID = 1;
if t==1
tracks_struct_PF.StateCovariance = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 1 0 0 0; 0 0 0 1 0 0; 0 0 0 0 0 0 ; 0 0 0 0 0 0 ];
else
pf_cov = [pf_tot{t};zeros(2,4)];
pf_cov2 = [pf_cov, zeros(6,2)];
tracks_struct_KM.StateCovariance = pf_cov2;
end
tracks_PF_cell{t} = tracks_struct_PF;
end
%%
tom = trackOSPAMetric;
ospa = zeros(numel(tracks_PF_cell),1);
cardOspa = zeros(numel(tracks_PF_cell),1);
locOspa = zeros(numel(tracks_PF_cell),1);
for ik = 1:numel(tracks_PF_cell)
ik
tracks_mi = tracks_PF_cell{ik}
truths_mi = truths_cell{ik}
[ospa(ik), locOspa(ik), cardOspa(ik)] = tom(tracks_mi, truths_mi);
end
%%
% Compute square error between truth and track locations
error_pf = (selected_traj(:, 1) – estimated_positions_pf(:, 1)).^2 + …
(selected_traj(:, 2) – estimated_positions_pf(:, 3)).^2;
Here is the error:
Error using assignjv
Expected COSTMATRIX to be non-NaN.
Error in fusion.internal.assignment.lapCheckCostMatrix (line 10)
validateattributes(costMatrix, {‘single’,’double’}, …
Error in assignjv (line 110)
fusion.internal.assignment.lapCheckCostMatrix(costMatrix, mfilename);
Error in fusion.internal.metrics.OSPABase/stepImpl (line 219)
optimalAssignment = assignjv(dMatrix, 2*cN);
Error in trackOSPAMetric/stepImpl (line 305)
[locOspa, cardOspa, optimalAssignment] = stepImpl@fusion.internal.metrics.OSPABase(obj, tracks, truths);
Error in OnlyPF (line 139)
[ospa(ik), locOspa(ik), cardOspa(ik)] = tom(tracks_mi, truths_mi);
>>Hello. I want to use trackOSPAMetric to evaluate tracking result with artificial data. but it does not match my data. I attached the code and the error. any ideas? thank you very much for your time and attention.
clc; clear; close;
% 2DPF+3DOSPA
% Parameters
num_trajectories = 4;
num_paths = 2;
points_per_trajectory = 100;
num_clusters = 60;
% Define base paths as nearly horizontal lines
base_paths = { [linspace(0, 10, points_per_trajectory)’, 2 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 4 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 6 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 8 * ones(points_per_trajectory, 1)] };
% Initialize trajectories
trajectories = cell(1, num_trajectories);
% Generate trajectories with random deviations from base paths
for ii = 1:num_trajectories
path = base_paths{mod(ii, num_paths) + 1};
deviation = [0.01 * randn(points_per_trajectory, 1), …
0.01 * randn(points_per_trajectory, 1)];
trajectories{ii} = path + deviation;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Select trajectory for tracking
selected_traj = trajectories{1};
numParticles = 1000;
stateDimension = 4;
init_state = [selected_traj(1, 1); 0;…
selected_traj(1, 2); 0];
pf = trackingPF(@constvel, @cvmeas, init_state, …
‘StateCovariance’, 0.5 * eye(stateDimension), …
‘NumParticles’, numParticles, …
‘HasAdditiveProcessNoise’, true);
% Particle filter tracking
estimated_positions_pf = …
zeros(size(selected_traj, 1), stateDimension);
pf_tot = cell(size(selected_traj, 1),1);
estimated_positions_pf(1, 🙂 = init_state’;
pf_no_km = clone(pf);
for t = 2:size(selected_traj, 1)
[xPred_pf, PPred_pf] = predict(pf_no_km, 1);
observation = …
[selected_traj(t, 1); selected_traj(t, 2); 0];
[xCorr_km, Pcorr_km] = correct(pf, observation);
[xCorr_pf, Pcorr_pf] = correct(pf_no_km, observation);
estimated_positions_pf(t, 🙂 = xCorr_pf;
pf_tot{t} = Pcorr_pf;
end
% Plot tracking results
figure (101);
plot(selected_traj(:, 1), selected_traj(:, 2), ‘g’, …
‘DisplayName’, ‘True Trajectory’);
hold on;
plot(estimated_positions_pf(:, 1), …
estimated_positions_pf(:, 3), ‘bo-.’, …
‘DisplayName’, ‘Estimated (No KM)’);
title(‘Tracking Map’);
legend;
xlabel(‘X’);
ylabel(‘Y’);
hold off;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% OSPA metric computation
truths_mine = …
[selected_traj zeros(size(selected_traj,1),1)];
%%
% Construct the truth and tracks structs
num_timesteps = size(selected_traj, 1);
truths_cell = cell(1,num_timesteps);
tracks_PF_cell = cell(1,num_timesteps);
% Compute velocities for truth data
velocities_truth = diff(truths_mine); % Approximate velocity (forward difference)
velocities_truth = [0 0 0;velocities_truth];
for t = 1:num_timesteps
% Truth struct
truth_struct = struct();
truth_struct.PlatformID = 1;
truth_struct.Time = t; % Time step
truth_struct.Position = …
[selected_traj(t, 1); selected_traj(t, 2);0]’; % Position [x; y]
% if t < num_timesteps
truth_struct.Velocity = velocities_truth(t, :); % Velocity [vx; vy]
truths_cell{t} = truth_struct;
% Tracks struct
tracks_struct_PF = struct();
tracks_struct_PF.UpdateTime = t; % Time step
tracks_struct_PF.State = …
[estimated_positions_pf(t, :),0,0]’;%; estimated_positions_pf(t, 3);0]; % Estimated position [x; y]
tracks_struct_PF.TrackID = 1;
if t==1
tracks_struct_PF.StateCovariance = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 1 0 0 0; 0 0 0 1 0 0; 0 0 0 0 0 0 ; 0 0 0 0 0 0 ];
else
pf_cov = [pf_tot{t};zeros(2,4)];
pf_cov2 = [pf_cov, zeros(6,2)];
tracks_struct_KM.StateCovariance = pf_cov2;
end
tracks_PF_cell{t} = tracks_struct_PF;
end
%%
tom = trackOSPAMetric;
ospa = zeros(numel(tracks_PF_cell),1);
cardOspa = zeros(numel(tracks_PF_cell),1);
locOspa = zeros(numel(tracks_PF_cell),1);
for ik = 1:numel(tracks_PF_cell)
ik
tracks_mi = tracks_PF_cell{ik}
truths_mi = truths_cell{ik}
[ospa(ik), locOspa(ik), cardOspa(ik)] = tom(tracks_mi, truths_mi);
end
%%
% Compute square error between truth and track locations
error_pf = (selected_traj(:, 1) – estimated_positions_pf(:, 1)).^2 + …
(selected_traj(:, 2) – estimated_positions_pf(:, 3)).^2;
Here is the error:
Error using assignjv
Expected COSTMATRIX to be non-NaN.
Error in fusion.internal.assignment.lapCheckCostMatrix (line 10)
validateattributes(costMatrix, {‘single’,’double’}, …
Error in assignjv (line 110)
fusion.internal.assignment.lapCheckCostMatrix(costMatrix, mfilename);
Error in fusion.internal.metrics.OSPABase/stepImpl (line 219)
optimalAssignment = assignjv(dMatrix, 2*cN);
Error in trackOSPAMetric/stepImpl (line 305)
[locOspa, cardOspa, optimalAssignment] = stepImpl@fusion.internal.metrics.OSPABase(obj, tracks, truths);
Error in OnlyPF (line 139)
[ospa(ik), locOspa(ik), cardOspa(ik)] = tom(tracks_mi, truths_mi);
>>Â Hello. I want to use trackOSPAMetric to evaluate tracking result with artificial data. but it does not match my data. I attached the code and the error. any ideas? thank you very much for your time and attention.
clc; clear; close;
% 2DPF+3DOSPA
% Parameters
num_trajectories = 4;
num_paths = 2;
points_per_trajectory = 100;
num_clusters = 60;
% Define base paths as nearly horizontal lines
base_paths = { [linspace(0, 10, points_per_trajectory)’, 2 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 4 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 6 * ones(points_per_trajectory, 1)];
[linspace(0, 10, points_per_trajectory)’, 8 * ones(points_per_trajectory, 1)] };
% Initialize trajectories
trajectories = cell(1, num_trajectories);
% Generate trajectories with random deviations from base paths
for ii = 1:num_trajectories
path = base_paths{mod(ii, num_paths) + 1};
deviation = [0.01 * randn(points_per_trajectory, 1), …
0.01 * randn(points_per_trajectory, 1)];
trajectories{ii} = path + deviation;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Select trajectory for tracking
selected_traj = trajectories{1};
numParticles = 1000;
stateDimension = 4;
init_state = [selected_traj(1, 1); 0;…
selected_traj(1, 2); 0];
pf = trackingPF(@constvel, @cvmeas, init_state, …
‘StateCovariance’, 0.5 * eye(stateDimension), …
‘NumParticles’, numParticles, …
‘HasAdditiveProcessNoise’, true);
% Particle filter tracking
estimated_positions_pf = …
zeros(size(selected_traj, 1), stateDimension);
pf_tot = cell(size(selected_traj, 1),1);
estimated_positions_pf(1, 🙂 = init_state’;
pf_no_km = clone(pf);
for t = 2:size(selected_traj, 1)
[xPred_pf, PPred_pf] = predict(pf_no_km, 1);
observation = …
[selected_traj(t, 1); selected_traj(t, 2); 0];
[xCorr_km, Pcorr_km] = correct(pf, observation);
[xCorr_pf, Pcorr_pf] = correct(pf_no_km, observation);
estimated_positions_pf(t, 🙂 = xCorr_pf;
pf_tot{t} = Pcorr_pf;
end
% Plot tracking results
figure (101);
plot(selected_traj(:, 1), selected_traj(:, 2), ‘g’, …
‘DisplayName’, ‘True Trajectory’);
hold on;
plot(estimated_positions_pf(:, 1), …
estimated_positions_pf(:, 3), ‘bo-.’, …
‘DisplayName’, ‘Estimated (No KM)’);
title(‘Tracking Map’);
legend;
xlabel(‘X’);
ylabel(‘Y’);
hold off;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% OSPA metric computation
truths_mine = …
[selected_traj zeros(size(selected_traj,1),1)];
%%
% Construct the truth and tracks structs
num_timesteps = size(selected_traj, 1);
truths_cell = cell(1,num_timesteps);
tracks_PF_cell = cell(1,num_timesteps);
% Compute velocities for truth data
velocities_truth = diff(truths_mine); % Approximate velocity (forward difference)
velocities_truth = [0 0 0;velocities_truth];
for t = 1:num_timesteps
% Truth struct
truth_struct = struct();
truth_struct.PlatformID = 1;
truth_struct.Time = t; % Time step
truth_struct.Position = …
[selected_traj(t, 1); selected_traj(t, 2);0]’; % Position [x; y]
% if t < num_timesteps
truth_struct.Velocity = velocities_truth(t, :); % Velocity [vx; vy]
truths_cell{t} = truth_struct;
% Tracks struct
tracks_struct_PF = struct();
tracks_struct_PF.UpdateTime = t; % Time step
tracks_struct_PF.State = …
[estimated_positions_pf(t, :),0,0]’;%; estimated_positions_pf(t, 3);0]; % Estimated position [x; y]
tracks_struct_PF.TrackID = 1;
if t==1
tracks_struct_PF.StateCovariance = [1 0 0 0 0 0; 0 1 0 0 0 0; 0 0 1 0 0 0; 0 0 0 1 0 0; 0 0 0 0 0 0 ; 0 0 0 0 0 0 ];
else
pf_cov = [pf_tot{t};zeros(2,4)];
pf_cov2 = [pf_cov, zeros(6,2)];
tracks_struct_KM.StateCovariance = pf_cov2;
end
tracks_PF_cell{t} = tracks_struct_PF;
end
%%
tom = trackOSPAMetric;
ospa = zeros(numel(tracks_PF_cell),1);
cardOspa = zeros(numel(tracks_PF_cell),1);
locOspa = zeros(numel(tracks_PF_cell),1);
for ik = 1:numel(tracks_PF_cell)
ik
tracks_mi = tracks_PF_cell{ik}
truths_mi = truths_cell{ik}
[ospa(ik), locOspa(ik), cardOspa(ik)] = tom(tracks_mi, truths_mi);
end
%%
% Compute square error between truth and track locations
error_pf = (selected_traj(:, 1) – estimated_positions_pf(:, 1)).^2 + …
(selected_traj(:, 2) – estimated_positions_pf(:, 3)).^2;
Here is the error:
Error using assignjv
Expected COSTMATRIX to be non-NaN.
Error in fusion.internal.assignment.lapCheckCostMatrix (line 10)
validateattributes(costMatrix, {‘single’,’double’}, …
Error in assignjv (line 110)
fusion.internal.assignment.lapCheckCostMatrix(costMatrix, mfilename);
Error in fusion.internal.metrics.OSPABase/stepImpl (line 219)
optimalAssignment = assignjv(dMatrix, 2*cN);
Error in trackOSPAMetric/stepImpl (line 305)
[locOspa, cardOspa, optimalAssignment] = stepImpl@fusion.internal.metrics.OSPABase(obj, tracks, truths);
Error in OnlyPF (line 139)
[ospa(ik), locOspa(ik), cardOspa(ik)] = tom(tracks_mi, truths_mi);
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