Add track age, total visible count, total invisible count in MOT

Question

alex 2023-8-7

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2005852-add-track-age-total-visible-count-total-invisible-count-in-mot

Hello, in the following MOT Matlab code I want to see and save the 'age', 'totalVisibleCount', and 'consecutiveInvisibleCount' of each track in the tracks struct. I also want to set this code in a way that, by using an assignment threshold, lost tracks are deleted. I would appreciate it if anyone could help me.

clc; clear; close;

% Load the object detections from store_box_score_label_center cell array

load('store_box_score_label_center.mat'); % detections are in a 4*N cell where N is the number of frames. first row = bboxs, second row = scores, third row = labels and last row = centers of bboxes

detections = store_box_score_label_center(4, :);

% Initialize the tracks

tracks = [];

track_id = 1;

% Process each frame

numFrames = size(detections, 2);

% Initialize the cell array to store centroids for all frames

allCentroidsTRACKS = cell(1, numFrames);

for t = 1:numFrames

% Get the current detections (centroids)

currentCentroids = detections{t};

% Predict the next location of the tracks

for i = 1:length(tracks)

tracks(i).centroid = kalman_predict(tracks(i).kalmanFilter);

end

% Calculate the cost matrix using centroids

costMatrix = calculateCostMatrix(tracks, currentCentroids);

AssignmentThreshold = 300;

% Assign detections to tracks using the Hungarian algorithm

[assignments, unassignedTracks, unassignedDetections] = ...

assignDetectionsToTracks(costMatrix, AssignmentThreshold);

% Update the assigned tracks

for ij = 1:size(assignments, 1)

trackIdx = assignments(ij, 1);

detectionIdx = assignments(ij, 2);

centroid1 = currentCentroids(detectionIdx, :);

tracks(trackIdx).kalmanFilter = kalman_correct(tracks(trackIdx).kalmanFilter, centroid1);

tracks(trackIdx).centroid = centroid1;

end

% Delete unassigned tracks

tracks(unassignedTracks) = [];

% Create new tracks for unassigned detections

for hh = 1:length(unassignedDetections)

detectionIdx2 = unassignedDetections(hh);

centroid = currentCentroids(detectionIdx2, :);

kalmanFilter = kalman_initialize(centroid);

newTrack = struct('id', track_id,...

'centroid', centroid, 'bbox', ...

store_box_score_label_center{1,t}(detectionIdx2, :),...

'kalmanFilter', kalmanFilter);

track_id = track_id + 1;

tracks = [tracks; newTrack];

end

% Store the centroids for the current frame

allCentroidsTRACKS{t} = vertcat(tracks(:).centroid);

allTrackIDS{t} = vertcat(tracks(:).id);

allTrackBBOXES{t} = vertcat(tracks(:).bbox);

save_tracks{t} = tracks;

% Plot the centroids for the current frame

figure(1);

image_show = imread(sprintf('frame_%04d.png', t+423));

imshow(image_show);

hold on;

colors = jet(length(tracks)); % Generate a colormap for the tracks

track_centroids = allCentroidsTRACKS{t};

trackIDs = allTrackIDS{t};

for io = 1:size(track_centroids, 1)

trackIdx1 = find([tracks.id] == trackIDs(io));

plot(track_centroids(io, 1), track_centroids(io, 2),...

'o', 'Color', colors(trackIdx1, :));

text(double(track_centroids(io, 1)),double(track_centroids(io, 2)),...

num2str(trackIDs(io)), 'Color', colors(trackIdx1, :),...

'FontSize', 8);

end

hold off;

saveas(gcf, fullfile('out_track2', sprintf('figure_%d.png', t+423 )));

end

%%

% Kalman filter functions

function kf = kalman_initialize(initial_state)

kf.x = [initial_state(1); 0; initial_state(2); 0]; % [x; vx; y; vy]

kf.P = eye(4) * 1e5;

kf.F = [1 1 0 0; 0 1 0 0; 0 0 1 1; 0 0 0 1];

kf.H = [1 0 0 0; 0 0 1 0];

kf.R = [25 0; 0 25];

kf.Q = eye(4) * 100;

end

function predicted_state = kalman_predict(kf)

kf.x = kf.F * kf.x;

kf.P = kf.F * kf.P * kf.F' + kf.Q;

predicted_state = kf.H * kf.x;

end

function kf = kalman_correct(kf, measurement)

y = measurement' - kf.H * kf.x;

S = kf.H * kf.P * kf.H' + kf.R;

K = kf.P * kf.H' / S;

kf.x = kf.x + K * y;

kf.P = (eye(4) - K * kf.H) * kf.P;

end

function costMatrix = calculateCostMatrix(tracks, centroids)

numTracks = length(tracks);

numCentroids = size(centroids, 1);

costMatrix = zeros(numTracks, numCentroids);

for i = 1:numTracks

trackCentroid = tracks(i).centroid;

for j = 1:numCentroids

detectionCentroid = centroids(j, :);

costMatrix(i, j) = pdist2(trackCentroid(:)', detectionCentroid(:)');

costMatrix(i, j);

end

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