目标障碍物检测跟踪matlab代码

以下是一份基于MATLAB的目标障碍物检测跟踪代码，供您参考：

% 读入视频
videoFileReader = vision.VideoFileReader('input.avi');

% 创建视频播放器
videoPlayer = vision.VideoPlayer('Position', [29, 597, 643, 386]);

% 读取第一帧图像
objectFrame = step(videoFileReader);

% 提取图像中的前景
foregroundDetector = vision.ForegroundDetector('NumGaussians', 3, 'NumTrainingFrames', 50);
foreground = step(foregroundDetector, objectFrame);

% 创建二值化形态学操作对象
se = strel('square', 3);

% 初始化跟踪器
objectRegion = [70, 50, 20, 20];
objectImage = insertShape(objectFrame, 'Rectangle', objectRegion, 'Color', 'red');
objectImage = rgb2gray(objectImage);
points = detectSURFFeatures(objectImage);
[features, points] = extractFeatures(objectImage, points);
tracker = vision.PointTracker('MaxBidirectionalError', 2, 'NumPyramidLevels', 5);
initialize(tracker, points.Location, objectImage);

% 跟踪视频中的目标
while ~isDone(videoFileReader)
    % 读取下一帧图像
    frame = step(videoFileReader);
    
    % 提取图像中的前景
    foreground = step(foregroundDetector, frame);
    
    % 进行形态学操作
    filteredForeground = imopen(foreground, se);
    
    % 检测并跟踪目标
    [points, validity] = step(tracker, rgb2gray(frame));
    trackedPoints = points(validity, :);
    if size(trackedPoints, 1) >= 2
        % 估计目标的边界框
        box = [min(trackedPoints(:, 1)), min(trackedPoints(:, 2)), max(trackedPoints(:, 1)) - min(trackedPoints(:, 1)), max(trackedPoints(:, 2)) - min(trackedPoints(:, 2))];
        
        % 显示跟踪结果
        objectImage = insertShape(frame, 'Rectangle', box, 'Color', 'red');
        step(videoPlayer, objectImage);
    end
end

% 释放资源
release(videoPlayer);
release(videoFileReader);

该代码使用了背景差分算法提取图像中的前景，然后通过形态学操作对前景进行滤波，最后使用SURF特征点检测和点追踪算法来跟踪目标。您可以将其作为参考，根据自己的需求进行修改和优化。