最近学习了一下多目标跟踪，看了看MathWorks的关于Motion-Based Multiple Object Tracking的Documention。
官网链接：http://cn.mathworks.com/help/vision/examples/motion-based-multiple-object-tracking.html?s_tid=gn_loc_drop
程序来自matlab的CV工具箱Computer Vision System Toolbox。这种方法用于静止背景下的多目标检测与跟踪。
程序可以分为两部分，1.每一帧检测运动objects；
2.实时的将检测得到的区域匹配到相同一个物体；
下面贴上matlab的demo，大家可以跑一跑。
function multiObjectTracking()
% create system objects used for reading video, detecting moving objects,
% and displaying the results
obj = setupSystemObjects(); %初始化函数
tracks = initializeTracks(); % create an empty array of tracks %初始化轨迹对象
nextId = 1; % ID of the next track
% detect moving objects, and track them across video frames
while ~isDone(obj.reader)
frame = readFrame(); %读取一帧
[centroids, bboxes, mask] = detectObjects(frame); %前景检测
predictNewLocationsOfTracks(); %根据位置进行卡尔曼预测
[assignments, unassignedTracks, unassignedDetections] = ...
detectionToTrackAssignment(); %匈牙利匹配算法进行匹配
updateAssignedTracks();%分配好的轨迹更新
updateUnassignedTracks();%未分配的轨迹更新
deleteLostTracks();%删除丢掉的轨迹
createNewTracks();%创建新轨迹
displayTrackingResults();%结果展示
end
%% Create System Objects
% Create System objects used for reading the video frames, detecting
% foreground objects, and displaying results.
function obj = setupSystemObjects()
% Initialize Video I/O
% Create objects for reading a video from a file, drawing the tracked
% objects in each frame, and playing the video.
% create a video file reader
obj.reader = vision.VideoFileReader('atrium.avi'); %读入视频
% create two video players, one to display the video,
% and one to display the foreground mask
obj.videoPlayer = vision.VideoPlayer('Position', [20, 400, 700, 400]); %创建两个窗口
obj.maskPlayer = vision.VideoPlayer('Position', [740, 400, 700, 400]);
% Create system objects for foreground detection and blob analysis
% The foreground detector is used to segment moving objects from
% the background. It outputs a binary mask, where the pixel value
% of 1 corresponds to the foreground and the value of 0 corresponds
% to the background.
obj.detector = vision.ForegroundDetector('NumGaussians', 3, ... %GMM进行前景检测，高斯核数目为3，前40帧为背景帧，域值为0.7
'NumTrainingFrames', 40, 'MinimumBackgroundRatio', 0.7);
% Connected groups of foreground pixels are likely to correspond to moving
% objects. The blob analysis system object is used to find such groups
% (called 'blobs' or 'connected components'), and compute their
% characteristics, such as area, centroid, and the bounding box.
obj.blobAnalyser = vision.BlobAnalysis('BoundingBoxOutputPort', true, ... %输出质心和外接矩形
'AreaOutputPort', true, 'CentroidOutputPort', true, ...
'MinimumBlobArea', 400);
end
%% Initialize Tracks
% The |initializeTracks| function creates an array of tracks, where each
% track is a structure representing a moving object in the video. The
% purpose of the structure is to maintain the state of a tracked object.
% The state consists of information used for detection to track assignment,
% track termination, and display.
%
% 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).
%
% Noisy detections tend to result in short-lived tracks. For this reason,
% the example only displays an object after it was tracked for some number
% of frames. This happens when |totalVisibleCount| exceeds a specified
% threshold.
%
% When no detections are associated with a track for several consecutive
% frames, the example assumes that the object has left the field of view
% and deletes the track. This happens when |consecutiveInvisibleCount|
% exceeds a specified threshold. A track may also get deleted as noise if
% it was tracked for a short time, and marked invisible for most of the of
% the frames.
function tracks = initializeTracks()
% create an empty array of tracks
tracks = struct(...
'id', {}, ... %轨迹ID
'bbox', {}, ... %外接矩形
'kalmanFilter', {}, ...%轨迹的卡尔曼滤波器
'age', {}, ...%总数量
'totalVisibleCount', {}, ...%可视数量
'consecutiveInvisibleCount', {});%不可视数量
end
%% Read a Video Frame
% Read the next video frame from the video file.
function frame = readFrame()
frame = obj.reader.step();%激活读图函数
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.
%
% The function performs motion segmentation using the foreground detector.
% It then performs morphological operations on the resulting binary mask to
% remove noisy pixels and to fill the holes in the remaining blobs.
function [centroids, bboxes, mask] = detectObjects(frame)
% detect foreground
mask = obj.detector.step(frame);
% apply morphological operations to remove noise and fill in holes
mask = imopen(mask, strel('rectangle', [3,3]));%开运算
mask = imclose(mask, strel('rectangle', [15, 15])); %闭运算
mask = imfill(mask, 'holes');%填洞
% perform blob analysis to find connected components
[~, centroids, bboxes] = obj.blobAnalyser.step(mask);
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);%根据以前的轨迹，预测当前位置
% 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)];%真正的当前位置
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.
%
% The algorithm involves two steps:
%
% Step 1: Compute the cost of assigning every detection to each track using
% the |distance| method of the |vision.KalmanFilter| System object. The
% cost takes into account the Euclidean distance between the predicted
% centroid of the track and the centroid of the detection. It also includes
% the confidence of the prediction, which is maintained by the Kalman
% filter. The results are stored in an MxN matrix, where M is the number of
% tracks, and N is the number of detections.
%
% Step 2: Solve the assignment problem represented by the cost matrix using
% the |assignDetectionsToTracks| function. The function takes the cost
% matrix and the cost of not assigning any detections to a track.
%
% The value for the cost of not assigning a detection to a track depends on
% the range of values returned by the |distance| method of the
% |vision.KalmanFilter|. This value must be tuned experimentally. Setting
% it too low increases the likelihood of creating a new track, and may
% result in track fragmentation. Setting it too high may result in a single
% track corresponding to a series of separate moving objects.
%
% The |assignDetectionsToTracks| function uses the Munkres' version of the
% Hungarian algorithm to compute an assignment which minimizes the total
% cost. It returns an M x 2 matrix containing the corresponding indices of
% assigned tracks and detections in its two columns. It also returns the
% indices of tracks and detections that remained unassigned.
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);
for i = 1:nTracks
cost(i, :) = distance(tracks(i).kalmanFilter, centroids);%损失矩阵计算
end
% solve the assignment problem
costOfNonAssignment = 20;
[assignments, unassignedTracks, unassignedDetections] = ...
assignDetectionsToTracks(cost, costOfNonAssignment);%匈牙利算法匹配
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
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;
end
end
%% Update Unassigned Tracks
% Mark each unassigned track as invisible, and increase its age by 1.
function updateUnassignedTracks()
for i = 1:length(unassignedTracks)
ind = unassignedTracks(i);
tracks(ind).age = tracks(ind).age + 1;
tracks(ind).consecutiveInvisibleCount = ...
tracks(ind).consecutiveInvisibleCount + 1;
end
end
%% Delete Lost Tracks
% The |deleteLostTracks| function deletes tracks that have been invisible
% for too many consecutive frames. It also deletes recently created tracks
% that have been invisible for too many frames overall.
function deleteLostTracks()
if isempty(tracks)
return;
end
invisibleForTooLong = 10;
ageThreshold = 8;
% compute the fraction of the track's age for which it was visible
ages = [tracks(:).age];
totalVisibleCounts = [tracks(:).totalVisibleCount];
visibility = totalVisibleCounts ./ ages;
% find the indices of 'lost' tracks
lostInds = (ages < ageThreshold & visibility < 0.6) | ...
[tracks(:).consecutiveInvisibleCount] >= invisibleForTooLong;
% delete lost tracks
tracks = tracks(~lostInds);
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], 100);
% create a new track
newTrack = struct(...
'id', nextId, ...
'bbox', bbox, ...
'kalmanFilter', kalmanFilter, ...
'age', 1, ...
'totalVisibleCount', 1, ...
'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);
% draw on the frame
frame = insertObjectAnnotation(frame, 'rectangle', ...
bboxes, labels);
% draw on the mask
mask = insertObjectAnnotation(mask, 'rectangle', ...
bboxes, labels);
end
end
% display the mask and the frame
obj.maskPlayer.step(mask);
obj.videoPlayer.step(frame);
end
%% Summary
% This example created a motion-based system for detecting and
% tracking multiple moving objects. Try using a different video to see if
% you are able to detect and track objects. Try modifying the parameters
% for the detection, assignment, and deletion steps.
%
% The tracking in this example was solely based on motion with the
% assumption that all objects move in a straight line with constant speed.
% When the motion of an object significantly deviates from this model, the
% example may produce tracking errors. Notice the mistake in tracking the
% person labeled #12, when he is occluded by the tree.
%
% The likelihood of tracking errors can be reduced by using a more complex
% motion model, such as constant acceleration, or by using multiple Kalman
% filters for every object. Also, you can incorporate other cues for
% associating detections over time, such as size, shape, and color.
displayEndOfDemoMessage(mfilename)
end