基于matlab多运动目标跟踪监测

%% Motion-Based Multiple Object Tracking % This example shows how to perform automatic detection and motion-based % tracking of moving objects in a video from a stationary camera. % % Copyright 2014 The MathWorks, Inc. %% % Detection of moving objects and motion-based tracking are important % components of many computer vision applications, including activity % recognition, traffic monitoring, and automotive safety. The problem % of motion-based object tracking can be divided into two parts: % % # Detecting moving objects in each frame % # Associating the detections corresponding to the same object over time % % The detection of moving objects uses a background subtraction algorithm % based on Gaussian mixture models. Morphological operations are applied to % the resulting foreground mask to eliminate noise. Finally, blob analysis % detects groups of connected pixels, which are likely to correspond to % moving objects. % % The association of detections to the same object is based solely on % motion. The motion of each track is estimated by a Kalman filter. The % filter is used to predict the track's location in each frame, and % determine the likelihood of each detection being assigned to each % track. % % Track maintenance becomes an important aspect of this example. In any % given frame, some detections may be assigned to tracks, while other % detections and tracks may remain unassigned. The assigned tracks are % updated using the corresponding detections. The unassigned tracks are % marked invisible. An unassigned detection begins a new track. % % Each track keeps count of the number of consecutive frames, where it % remained unassigned. If the count exceeds a specified threshold, the % example assumes that the object left the field of view and it deletes % the track. % % For more information please see % <docid:vision_ug#buq9qny-1 Multiple Object Tracking>. % % This example is a function with the main body at the top and helper % routines in the form of nested functions. function MotionBasedMultiObjectTrackingExample() % 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. while hasFrame(obj.reader) frame = readFrame(obj.reader); [centroids, bboxes, mask] = detectObjects(frame); predictNewLocationsOfTracks(); [assignments, unassignedTracks, unassignedDetections] = ... detectionToTrackAssignment(); updateAssignedTracks(); updateUnassignedTracks(); deleteLostTracks(); createNewTracks(); displayTrackingResults(); 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 reader. obj.reader = VideoReader('atrium.mp4'); % Create two video players, one to display the video, % and one to display the foreground mask. obj.maskPlayer = vision.VideoPlayer('Position', [740, 400, 700, 400]); obj.videoPlayer = vision.VideoPlayer('Position', [20, 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, ... '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, ... '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 % frames. function tracks = initializeTracks() % create an empty array of tracks tracks = struct(... 'id', {}, ... 'bbox', {}, ... 'kalmanFilter', {}, ... 'age', {}, ... 'totalVisibleCount', {}, ... 'consecutiveInvisibleCount', {}); 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])); mask = imclose(mask, strel('rectangle', [15, 15])); mask = imfill(mask, 'holes'); % 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).kalm