Matlabscript Botsingsdetectie
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Hieronder staat de Matlab-code voor de botsingsdetectie. Comments over gebruikte code staan in het script erbij vermeldt.
function Collision_detection()
clc
% Create System objects used for reading video, detecting moving objects,
% and displaying the results.
obj = setupSystemObjects();
tracks = initializeTracks(); % Create an empty array of tracks.
nextId = 1; % ID of the next track
% Detect moving objects, and track them across video frames.
fr=0;
disp('Starting...');
f = step(obj.reader);
obj.videoPlayer.step(f); release(obj.videoPlayer);
Lastpos = [0 0];
Currentpos = [0 0];
colvar = 1;
SHOW = 1; %turn on/off message box
while fr < 41 && isOpen(obj.videoPlayer)
fr;
frame = readFrame();
[centroids, bboxes, mask] = detectObjects(frame, blob);
predictNewLocationsOfTracks();
[assignments, ~ , unassignedDetections] = ...
detectionToTrackAssignment();
updateAssignedTracks(); %position
createNewTracks();
displayTrackingResults();
fr=fr+1;
end
release(obj.videoPlayer);
obj.videoPlayer.hide();
close all;
%% Create System Objects
% Create System objects used for reading the video frames, detecting
% colored objects, and displaying results.
function obj = setupSystemObjects()
% Initialize Video
% Create objects for reading a video from a file, drawing the tracked
% objects in each frame, and playing the video. No live camera data
% can be analyzed yet
obj.reader = vision.VideoFileReader('botsing1.mp4');
% Create a video player
obj.videoPlayer = vision.VideoPlayer('Position', [200, 400, 700, 400]);
% Create System objects based on movement and blob analysis
obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
'NumTrainingFrames', 5, 'MinimumBackgroundRatio', 0.8);
blob = vision.BlobAnalysis('BoundingBoxOutputPort', true, 'ExcludeBorderBlobs',true, ...
'MajorAxisLengthOutputPort',true,'EccentricityOutputPort', true, 'CentroidOutputPort', true, ...
'MinimumBlobArea', 2100, 'MaximumBlobArea',50000);
end
%% Initialize Tracks
% The structure contains the following fields:
%
% * |id| : the integer ID of the track
% * |bbox| : the current bounding box of the object; used
% for display
% * |kalmanFilter| : a Kalman filter object used for motion-based
% tracking
% * |age| : the number of frames since the track was first
% detected
% * |totalVisibleCount| : the total number of frames in which the track
% was detected (visible)
% * |consecutiveInvisibleCount| : the number of consecutive frames for
% which the track was not detected (invisible).
% This results in deleting the track if the
% threshold is reached
function tracks = initializeTracks()
% create an empty array of tracks
tracks = struct(...
'id', {}, ...
'bbox', {}, ...
'kalmanFilter', {}, ...
'age', {}, ...
'totalVisibleCount', {}, ...
'consecutiveInvisibleCount', {});
end
%% Read a Video Frame
% Read the next video frame from the video file.
function frame = readFrame()
frame = obj.reader.step();
%frame = snapshot(cam);
end
%% Detect Objects
% The |detectObjects| function returns the centroids and the bounding boxes
% of the detected objects. It also returns the binary mask, which has the
% same size as the input frame. Pixels with a value of 1 correspond to the
% foreground, and pixels with a value of 0 correspond to the background.
function [centroids, bboxes, mask] = detectObjects(frame, blob)
% Use color to identify turtles from each team. Only these colors will
% be taken into account. A distinctive top color results in more
% accurate tracking.
Red = frame*255;
Red = Red(:,:,1)>Red(:,:,2)*2 & Red(:,:,1)>Red(:,:,2)*2;
mask = obj.detector.step(frame);
% Apply morphological operations to remove noise and fill in holes.
mask = imopen(mask, strel('rectangle', [9,9]));
mask = imclose(mask, strel('rectangle', [15, 15]));
mask = imfill(mask, 'holes');
% detect blobs, return centroids, bounding boxes, eccentricity and diameter
[~,centroids,bboxes,diam,ecc] = step(blob,Red);
% maximize for most round object
if ~isempty(centroids)
[~,I] = max(ecc,[],1);
bboxes = bboxes(I,:);
centroids = centroids(I,:);
diam = diam(I);
end
%check if max is indeed round (i.e. if anything useful detected)
if ecc > 1
ecc = [];
centroids = [];
bboxes = [];
diam = [];
end
end
%% Predict New Locations of Existing Tracks
% Use the Kalman filter to predict the centroid of each track in the
% current frame, and update its bounding box accordingly.
function predictNewLocationsOfTracks()
for i = 1:length(tracks)
bbox = tracks(i).bbox;
% Predict the current location of the track.
predictedCentroid = predict(tracks(i).kalmanFilter);
% Shift the bounding box so that its center is at
% the predicted location.
predictedCentroid = int32(predictedCentroid) - bbox(3:4) / 2;
tracks(i).bbox = [predictedCentroid, bbox(3:4)];
end
end
%% Assign Detections to Tracks
% Assigning object detections in the current frame to existing tracks is
% done by minimizing cost. The cost is defined as the negative
% log-likelihood of a detection corresponding to a track.
function [assignments, unassignedTracks, unassignedDetections] = ...
detectionToTrackAssignment()
nTracks = length(tracks);
nDetections = size(centroids, 1);
% Compute the cost of assigning each detection to each track.
cost = zeros(nTracks, nDetections);
if nTracks>0
cost(1, :) = distance(tracks(1).kalmanFilter, centroids);
end
% Solve the assignment problem.
costOfNonAssignment = 20;
[assignments, unassignedTracks, unassignedDetections] = ...
assignDetectionsToTracks(cost, costOfNonAssignment);
end
%% Update Assigned Tracks
% The |updateAssignedTracks| function updates each assigned track with the
% corresponding detection. It calls the |correct| method of
% |vision.KalmanFilter| to correct the location estimate. Next, it stores
% the new bounding box, and increases the age of the track and the total
% visible count by 1. Finally, the function sets the invisible count to 0.
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 the estimate of the object's location
% using the new detection. This will give the current position
Currentpos = correct(tracks(trackIdx).kalmanFilter, centroid);
% Replace predicted bounding box with detected
% bounding box.
tracks(trackIdx).bbox = bbox;
% Update track's age.
tracks(trackIdx).age = tracks(trackIdx).age + 1;
% Update visibility.
tracks(trackIdx).totalVisibleCount = ...
tracks(trackIdx).totalVisibleCount + 1;
tracks(trackIdx).consecutiveInvisibleCount = 0;
checkcollision();
saveposition();
end
end
%% Create New Tracks
% Create new tracks from unassigned detections. Assume that any unassigned
% detection is a start of a new track. In practice, you can use other cues
% to eliminate noisy detections, such as size, location, or appearance.
function createNewTracks()
centroids = centroids(unassignedDetections, :);
bboxes = bboxes(unassignedDetections, :);
for i = 1:size(centroids, 1)
centroid = centroids(i,:);
bbox = bboxes(i, :);
% Create a Kalman filter object.
kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
centroid, [200, 50], [100, 25], 200);
% Create a new track.
newTrack = struct(...
'id', nextId, ...
'bbox', bbox, ...
'kalmanFilter', kalmanFilter, ...
'age', 1, ...
'totalVisibleCount', 4, ...
'consecutiveInvisibleCount', 0);
% Add it to the array of tracks.
tracks(end + 1) = newTrack;
% Increment the next id.
nextId = nextId + 1;
end
end
%% Display Tracking Results
% The |displayTrackingResults| function draws a bounding box and label ID
% for each track on the video frame and the foreground mask. It then
% displays the frame and the mask in their respective video players.
function displayTrackingResults()
% Convert the frame and the mask to uint8 RGB.
frame = im2uint8(frame);
mask = uint8(repmat(mask, [1, 1, 3])) .* 255;
minVisibleCount = 8;
if ~isempty(tracks)
% Noisy detections tend to result in short-lived tracks.
% Only display tracks that have been visible for more than
% a minimum number of frames.
reliableTrackInds = ...
[tracks(:).totalVisibleCount] > minVisibleCount;
reliableTracks = tracks(reliableTrackInds);
% Display the objects. If an object has not been detected
% in this frame, display its predicted bounding box.
if ~isempty(reliableTracks)
% Get bounding boxes.
bboxes = cat(1, reliableTracks.bbox);
% Get ids.
ids = int32([reliableTracks(:).id]);
% Create labels for objects indicating the ones for
% which we display the predicted rather than the actual
% location.
labels = cellstr(int2str(ids'));
predictedTrackInds = ...
[reliableTracks(:).consecutiveInvisibleCount] > 0;
isPredicted = cell(size(labels));
isPredicted(predictedTrackInds) = {' predicted'};
labels = strcat(labels, isPredicted);
labels = 'Red Ball';
% Draw the objects on the frame.
frame = insertObjectAnnotation(frame, 'rectangle', ...
bboxes, labels);
% Draw the objects on the mask.
mask = insertObjectAnnotation(mask, 'rectangle', ...
bboxes, labels);
end
end
% Display the mask and the frame.
obj.videoPlayer.step(frame);
end
%% Store previous location
% The |saveposition| function stores the location of the previous frame to
% allow for the calculation of a direction vector created from consecutive
% frames
function saveposition()
if fr <2
Lastpos = [0 0];
elseif fr >= 2
Lastpos = Currentpos;
end
end
%% Check for a collision
% The |checkcollision| function checks every frame if there is a sudden
% change in the direction compared to its previous. If more elaborate
% collision rules apply, one can look to find the acceleration
function checkcollision()
if ~isempty(Lastpos) && fr >17 %% Bounding box drawn
if (Currentpos(1) > Lastpos(1))&&(Currentpos(2) > Lastpos(2)) ...
&& colvar == 1
fprintf('BOTSING in frame %u \n', fr);
if SHOW == 1
h =msgbox('Collision occured!');
end
colvar = colvar + 1;
end
end
end
end
A link to the matlab code can be found here: link title
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