基于matlab多运动目标跟踪监测算法实现

function obj = setupSystemObjects()
        % 读取视频文件
        obj.reader = VideoReader('atrium.mp4');
        % 创建两个视频播放器，一个用于显示视频，一个用于显示前景蒙版
        obj.maskPlayer = vision.VideoPlayer('Position', [740, 400, 700, 400]);
        obj.videoPlayer = vision.VideoPlayer('Position', [20, 400, 700, 400]);
        % 创建用于前景检测和blob分析的系统对象
        obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
            'NumTrainingFrames', 40, 'MinimumBackgroundRatio', 0.7);
        obj.blobAnalyser = vision.BlobAnalysis('BoundingBoxOutputPort', true, ...
            'AreaOutputPort', true, 'CentroidOutputPort', true, ...
            'MinimumBlobArea', 400);
    end

function tracks = initializeTracks()
        % 创建一个空的轨迹数组
        tracks = struct(...
            'id', {}, ...
            'bbox', {}, ...
            'kalmanFilter', {}, ...
            'age', {}, ...
            'totalVisibleCount', {}, ...
            'consecutiveInvisibleCount', {});
    end

function [centroids, bboxes, mask] = detectObjects(frame)
        % 检测前景
        mask = obj.detector.step(frame);
        % 应用形态学操作来去除噪声并填充孔洞
        mask = imopen(mask, strel('rectangle', [3,3]));
        mask = imclose(mask, strel('rectangle', [15, 15]));
        mask = imfill(mask, 'holes');
        % 执行blob分析以查找连接的组件
        [~, centroids, bboxes] = obj.blobAnalyser.step(mask);
    end

function predictNewLocationsOfTracks()
        for i = 1:length(tracks)
            bbox = tracks(i).bbox;
            % 预测轨迹的当前位置
            predictedCentroid = predict(tracks(i).kalmanFilter);
            % 移动边界框，使其中心位于预测的位置
            predictedCentroid = int32(predictedCentroid) - bbox(3:4) / 2;
            tracks(i).bbox = [predictedCentroid, bbox(3:4)];
        end
    end

function [assignments, unassignedTracks, unassignedDetections] = ...
            detectionToTrackAssignment()
        nTracks = length(tracks);
        nDetections = size(centroids, 1);
        % 计算将每个检测分配给每个轨迹的成本
        cost = zeros(nTracks, nDetections);
        for i = 1:nTracks
            cost(i, :) = distance(tracks(i).kalmanFilter, centroids);
        end
        % 解决分配问题
        costOfNonAssignment = 20;
        [assignments, unassignedTracks, unassignedDetections] = ...
            assignDetectionsToTracks(cost, 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, :);
            % 更正对象位置的估计值
            correct(tracks(trackIdx).kalmanFilter, centroid);
            % 使用检测到的边界框替换预测的边界框
            tracks(trackIdx).bbox = bbox;
            % 更新age
            tracks(trackIdx).age = tracks(trackIdx).age + 1;
            % 更新visibility
            tracks(trackIdx).totalVisibleCount = ...
                tracks(trackIdx).totalVisibleCount + 1;
            tracks(trackIdx).consecutiveInvisibleCount = 0;
        end
    end

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

function deleteLostTracks()
        if isempty(tracks)
            return;
        end
        invisibleForTooLong = 20;
        ageThreshold = 8;
        % 计算轨迹visibility的age分数
        ages = [tracks(:).age];
        totalVisibleCounts = [tracks(:).totalVisibleCount];
        visibility = totalVisibleCounts ./ ages;
        % 查找“丢失”轨迹的索引
        lostInds = (ages < ageThreshold & visibility < 0.6) | ...
            [tracks(:).consecutiveInvisibleCount] >= invisibleForTooLong;
        % 删除丢失的轨迹
        tracks = tracks(~lostInds);
    end

function createNewTracks()
        centroids = centroids(unassignedDetections, :);
        bboxes = bboxes(unassignedDetections, :);
        for i = 1:size(centroids, 1)
            centroid = centroids(i,:);
            bbox = bboxes(i, :);
            % 创建卡尔曼滤波器对象
            kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
                centroid, [200, 50], [100, 25], 100);
            % 创建新轨迹
            newTrack = struct(...
                'id', nextId, ...
                'bbox', bbox, ...
                'kalmanFilter', kalmanFilter, ...
                'age', 1, ...
                'totalVisibleCount', 1, ...
                'consecutiveInvisibleCount', 0);
            % 将其添加到轨迹阵列中
            tracks(end + 1) = newTrack;
            % 递增下一个id
            nextId = nextId + 1;
        end
    end

function displayTrackingResults()
        % 将帧和mask转换为uint8 RGB。
        frame = im2uint8(frame);
        mask = uint8(repmat(mask, [1, 1, 3])) .* 255;
        minVisibleCount = 8;
        if ~isempty(tracks)
            reliableTrackInds = ...
                [tracks(:).totalVisibleCount] > minVisibleCount;
            reliableTracks = tracks(reliableTrackInds);
            % 显示对象
            if ~isempty(reliableTracks)
                % 获取边界框
                bboxes = cat(1, reliableTracks.bbox);
                % 获取ID
                ids = int32([reliableTracks(:).id]);
                % 为对象创建标签
                labels = cellstr(int2str(ids'));
                predictedTrackInds = ...
                    [reliableTracks(:).consecutiveInvisibleCount] > 0;
                isPredicted = cell(size(labels));
                isPredicted(predictedTrackInds) = {' predicted'};
                labels = strcat(labels, isPredicted);
                % 在frame上绘制对象
                frame = insertObjectAnnotation(frame, 'rectangle', ...
                    bboxes, labels);
                % 在mask上绘制对象
                mask = insertObjectAnnotation(mask, 'rectangle', ...
                    bboxes, labels);
            end
        end
        % 显示mask和frame
        obj.maskPlayer.step(mask);
        obj.videoPlayer.step(frame);
    end