cmt算法_sctp协议_cn算法

代码主要在CMT这个类里面,主要包含initialize和processFrame两个函数实现,跟论文契合的非常好,每一个成员时干啥的,我都注释在旁边了

代码下载链接点击打开链接

namespace cmt
{

class CMT
{
public:
    CMT() : str_detector("FAST"), str_descriptor("BRISK") {} //默认的特征检测和描述子

void initialize(const Mat im_gray, const Rect rect); //rect是初始目标框
    void processFrame(const Mat im_gray); 

    Fusion fusion;//跟踪点融合:融合跟踪和匹配的点 将两种点都放在一起，并且不重复
    Matcher matcher; //DescriptorMatcher 用的knnMatch匹配
    Tracker tracker; //光溜匹配跟踪 上一帧的特征点使用光流法跟踪得到这一帧的特征点的位置T
    Consensus consensus;//包括scale和rotation angel的求取和对目标中心的投票

    string str_detector;
    string str_descriptor;

    vector<Point2f> points_active; //public for visualization purposes
    RotatedRect bb_rot;

private:
    Ptr<FeatureDetector> detector;
    Ptr<DescriptorExtractor> descriptor;

    Size2f size_initial;

    vector<int> classes_active; //forgroud points -in target boundingboxs

    float theta;

    Mat im_prev;
};

} /* namespace CMT */

最重要的跟踪实现流程:

初始化的代码实现,加注释:

void CMT::initialize(const Mat im_gray, const Rect rect)
{

    //Remember initial size
    size_initial = rect.size();

    //Remember initial image
    im_prev = im_gray;

    //Compute center of rect
    Point2f center = Point2f(rect.x + rect.width/2.0, rect.y + rect.height/2.0);

    //Initialize rotated bounding box
    bb_rot = RotatedRect(center, size_initial, 0.0);

    //Initialize detector and descriptor

    //FeatureDetector is OpenCVs feature detector,including "FAST","ORB","SIFT"
    //"STAR",MSER","GFTT","HARRIS","Dense","SimpleBlob"
    detector = FeatureDetector::create(str_detector);//FAST
    descriptor = DescriptorExtractor::create(str_descriptor);//BRISK

    //Get initial keypoints in whole image and compute their descriptors
    vector<KeyPoint> keypoints;
    detector->detect(im_gray, keypoints);

    //Divide keypoints into foreground and background keypoints according to selection
    //in target bounding box is foreground
    vector<KeyPoint> keypoints_fg;
    vector<KeyPoint> keypoints_bg;

    for (size_t i = 0; i < keypoints.size(); i++)
    {
        KeyPoint k = keypoints[i];
        Point2f pt = k.pt;

        if (pt.x > rect.x && pt.y > rect.y && pt.x < rect.br().x && pt.y < rect.br().y)
        {
            keypoints_fg.push_back(k);
        }

        else
        {
            keypoints_bg.push_back(k);
        }

    }

    //Create foreground classes
    vector<int> classes_fg;
    classes_fg.reserve(keypoints_fg.size());
    for (size_t i = 0; i < keypoints_fg.size(); i++)
    {
        classes_fg.push_back(i);
    }

    //Compute foreground/background features
    Mat descs_fg;
    Mat descs_bg;
    descriptor->compute(im_gray, keypoints_fg, descs_fg);
    descriptor->compute(im_gray, keypoints_bg, descs_bg);

    //Only now is the right time to convert keypoints to points, as compute() might remove some keypoints
    vector<Point2f> points_fg;
    vector<Point2f> points_bg;

    for (size_t i = 0; i < keypoints_fg.size(); i++)
    {
        points_fg.push_back(keypoints_fg[i].pt);
    }


    for (size_t i = 0; i < keypoints_bg.size(); i++)
    {
        points_bg.push_back(keypoints_bg[i].pt);
    }

    //Create normalized points: distance to center
    vector<Point2f> points_normalized;
    for (size_t i = 0; i < points_fg.size(); i++)
    {
        points_normalized.push_back(points_fg[i] - center);
    }

    //Initialize matcher
    matcher.initialize(points_normalized, descs_fg, classes_fg, descs_bg, center);

    //Initialize consensus get Xi,Xj distance and angle
    consensus.initialize(points_normalized);

    //Create initial set of active keypoints
    for (size_t i = 0; i < keypoints_fg.size(); i++)
    {
        points_active.push_back(keypoints_fg[i].pt);
        classes_active = classes_fg;
    }

}

总结:这是一个基于特征点匹配的跟踪算法,创新之处在于使用了特征点的相对距离的投票来确定目标位置.优势在于对于不形变的物体而言，不管物体怎么移动旋转，其上面的特征点相对中心的距离是在缩放比例下是确定的.

作者在论文中这样说:The main idea behind CMT is to break down the object of interest into tiny parts, known as keypoints.In each frame, we try to again find the keypoints that were already therein the initial selection of the object of interest.We do this by employing two different kind of methods.First, we track keypoints from the previous frame to the current frame by estimatingwhat is known as itsoptic flow.Second, we match keypoints globally by comparing theirdescriptors.As both of these methods are error-prone, we employ a novel way of looking for consensus withinthe found keypoints by letting each keypoint vote for the object center,as shown in the following image:

而后通过对中心的聚类投票确定中心位置:

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