Matlab. How to implement tracking with counter on top of boxes? - matlab

Question

Hello I have tried using the example in
https://www.mathworks.com/help/vision/examples/tracking-pedestrians-from-a-moving-car.html and now I want to add a counter to replace the score. I have tried mixing the example in https://www.mathworks.com/help/vision/examples/motion-based-multiple-object-tracking.html. I have tried using the method it use to add in counter above the box. but to no avil.
Below are the codes.
function PedestrianTrackingFromMovingCameraExample()
videoFile = 'vippedtracking.mp4';
scaleDataFile = 'pedScaleTable.mat'; % An auxiliary file that helps to determine the size of a pedestrian at different pixel locations.
video = VideoReader(videoFile);
obj = setupSystemObjects(videoFile, scaleDataFile);
detector = peopleDetectorACF('caltech');
tracks = initializeTracks();
nextId = 1;
option.scThresh = 0.3;
option.gatingThresh = 0.9;
option.gatingCost = 100;
option.costOfNonAssignment = 10;
option.timeWindowSize = 16;
option.confidenceThresh = 2;
option.ageThresh = 8;
option.visThresh = 0.6;
while hasFrame(video)
frame = readFrame();
[centroids, bboxes, scores] = detectPeople();
predictNewLocationsOfTracks();
[assignments, unassignedTracks, unassignedDetections] = ...
detectionToTrackAssignment();
updateAssignedTracks();
updateUnassignedTracks();
deleteLostTracks();
createNewTracks();
displayTrackingResults();
if ~isOpen(obj.videoPlayer)
break;
end
end
function obj = setupSystemObjects(videoFile,scaleDataFile)
obj.reader = vision.VideoFileReader(videoFile, 'VideoOutputDataType', 'uint8');
obj.videoPlayer = vision.VideoPlayer('Position', [29, 597, 643, 386]);
ld = load(scaleDataFile, 'pedScaleTable');
obj.pedScaleTable = ld.pedScaleTable;
end
function tracks = initializeTracks()
tracks = struct(...
'id', {}, ...
'color', {}, ...
'bboxes', {}, ...
'scores', {}, ...
'kalmanFilter', {}, ...
'age', {}, ...
'totalVisibleCount', {}, ...
'confidence', {}, ...
'predPosition', {}, ...
'consecutiveInvisibleCount', {});
end
function frame = readFrame()
frame = step(obj.reader);
end
function [centroids, bboxes, scores] = detectPeople()
resizeRatio = 1.5;
frame = imresize(frame, resizeRatio, 'Antialiasing',false);
[bboxes, scores] = detect(detector, frame, ...
'WindowStride', 2,...
'NumScaleLevels', 4, ...
'SelectStrongest', false);
height = bboxes(:, 4) / resizeRatio;
y = (bboxes(:,2)-1) / resizeRatio + 1;
yfoot = min(length(obj.pedScaleTable), round(y + height));
estHeight = obj.pedScaleTable(yfoot);
invalid = abs(estHeight-height)>estHeight*option.scThresh;
bboxes(invalid, :) = [];
scores(invalid, :) = [];
[bboxes, scores] = selectStrongestBbox(bboxes, scores, ...
'RatioType', 'Min', 'OverlapThreshold', 0.6);
if isempty(bboxes)
centroids = [];
else
centroids = [(bboxes(:, 1) + bboxes(:, 3) / 2), ...
(bboxes(:, 2) + bboxes(:, 4) / 2)];
end
end
function predictNewLocationsOfTracks()
for i = 1:length(tracks)
bbox = tracks(i).bboxes(end, :);
predictedCentroid = predict(tracks(i).kalmanFilter);
tracks(i).predPosition = [predictedCentroid - bbox(3:4)/2, bbox(3:4)];
end
end
function [assignments, unassignedTracks, unassignedDetections] = ...
detectionToTrackAssignment()
predBboxes = reshape([tracks(:).predPosition], 4, [])';
cost = 1 - bboxOverlapRatio(predBboxes, bboxes);
cost(cost > option.gatingThresh) = 1 + option.gatingCost;
[assignments, unassignedTracks, unassignedDetections] = ...
assignDetectionsToTracks(cost, option.costOfNonAssignment);
end
function updateAssignedTracks()
numAssignedTracks = size(assignments, 1);
for i = 1:numAssignedTracks
trackIdx = assignments(i, 1);
detectionIdx = assignments(i, 2);
centroid = centroids(detectionIdx, :);
bbox = bboxes(detectionIdx, :);
correct(tracks(trackIdx).kalmanFilter, centroid);
T = min(size(tracks(trackIdx).bboxes,1), 4);
w = mean([tracks(trackIdx).bboxes(end-T+1:end, 3); bbox(3)]);
h = mean([tracks(trackIdx).bboxes(end-T+1:end, 4); bbox(4)]);
tracks(trackIdx).bboxes(end+1, :) = [centroid - [w, h]/2, w, h];
tracks(trackIdx).age = tracks(trackIdx).age + 1;
tracks(trackIdx).scores = [tracks(trackIdx).scores; scores(detectionIdx)];
tracks(trackIdx).totalVisibleCount = ...
tracks(trackIdx).totalVisibleCount + 1;
tracks(trackIdx).consecutiveInvisibleCount = 0;
T = min(option.timeWindowSize, length(tracks(trackIdx).scores));
score = tracks(trackIdx).scores(end-T+1:end);
tracks(trackIdx).confidence = [max(score), mean(score)];
end
end
function updateUnassignedTracks()
for i = 1:length(unassignedTracks)
idx = unassignedTracks(i);
tracks(idx).age = tracks(idx).age + 1;
tracks(idx).bboxes = [tracks(idx).bboxes; tracks(idx).predPosition];
tracks(idx).scores = [tracks(idx).scores; 0];
tracks(idx).consecutiveInvisibleCount = ...
tracks(idx).consecutiveInvisibleCount + 1;
T = min(option.timeWindowSize, length(tracks(idx).scores));
score = tracks(idx).scores(end-T+1:end);
tracks(idx).confidence = [max(score), mean(score)];
end
end
function deleteLostTracks()
if isempty(tracks)
return;
end
ages = [tracks(:).age]';
totalVisibleCounts = [tracks(:).totalVisibleCount]';
visibility = totalVisibleCounts ./ ages;
confidence = reshape([tracks(:).confidence], 2, [])';
maxConfidence = confidence(:, 1);
lostInds = (ages <= option.ageThresh & visibility <= option.visThresh) | ...
(maxConfidence <= option.confidenceThresh);
tracks = tracks(~lostInds);
end
function createNewTracks()
unassignedCentroids = centroids(unassignedDetections, :);
unassignedBboxes = bboxes(unassignedDetections, :);
unassignedScores = scores(unassignedDetections);
for i = 1:size(unassignedBboxes, 1)
centroid = unassignedCentroids(i,:);
bbox = unassignedBboxes(i, :);
score = unassignedScores(i);
kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
centroid, [2, 1], [5, 5], 100);
newTrack = struct(...
'id', nextId, ...
'color', 255*rand(1,3), ...
'bboxes', bbox, ...
'scores', score, ...
'kalmanFilter', kalmanFilter, ...
'age', 1, ...
'totalVisibleCount', 1, ...
'confidence', [score, score], ...
'predPosition', bbox, ...
'consecutiveInvisibleCount', 0);
tracks(end + 1) = newTrack; %#ok<AGROW>
nextId = nextId + 1;
end
end
function displayTrackingResults()
displayRatio = 4/3;
frame = imresize(frame, displayRatio);
minVisibleCount = 8;
if ~isempty(tracks)
reliableTrackInds = ...
[tracks(:).totalVisibleCount] > minVisibleCount;
reliableTracks = tracks(reliableTrackInds);
ids = int32([reliableTracks(:).id]);
labels = cellstr(int2str(ids'));
predictedTrackInds = ...
[reliableTracks(:).consecutiveInvisibleCount] > 0;
isPredicted = cell(size(labels));
isPredicted(predictedTrackInds) = {'predicted'};
labels = strcat(labels, isPredicted);
ages = [tracks(:).age]';
confidence = reshape([tracks(:).confidence], 2, [])';
maxConfidence = confidence(:, 1);
avgConfidence = confidence(:, 2);
opacity = min(0.5,max(0.1,avgConfidence/3));
noDispInds = (ages < option.ageThresh & maxConfidence < option.confidenceThresh) | ...
(ages < option.ageThresh / 2);
for i = 1:length(tracks)
if ~noDispInds(i)
bb = tracks(i).bboxes(end, :);
bb(:,1:2) = (bb(:,1:2)-1)*displayRatio + 1;
bb(:,3:4) = bb(:,3:4) * displayRatio;
frame = insertObjectAnnotation(frame, ...
'rectangle', bb, ...
labels, ...
'Color', tracks(i).color);
end
end
end
step(obj.videoPlayer, frame);
end
end