Question

我用它来作为我的跟踪算法的基础。

    //1. detect the features
    cv::goodFeaturesToTrack(gray_prev, // the image 
    features,   // the output detected features
    max_count,  // the maximum number of features 
    qlevel,     // quality level
    minDist);   // min distance between two features

    // 2. track features
    cv::calcOpticalFlowPyrLK(
    gray_prev, gray, // 2 consecutive images
    points_prev, // input point positions in first im
    points_cur, // output point positions in the 2nd
    status,    // tracking success
    err);      // tracking error

cv::calcOpticalFlowPyrLK将前一图像中的点矢量作为输入，并在下一图像上返回适当的点。假设我在前一个图像上有随机像素（x，y），如何使用OpenCV光流功能计算下一个图像上该像素的位置？

Answer 1

在你写作时，cv::goodFeaturesToTrack将一个图像作为输入并产生一个它认为“很好跟踪”的点矢量。这些是根据他们从周围环境中脱颖而出的能力来选择的，并且基于图像中的哈里斯角落。通常通过将第一个图像传递给goodFeaturesToTrack并获得一组要跟踪的特征来初始化跟踪器。然后，这些特征可以作为前面的点传递给cv::calcOpticalFlowPyrLK，以及序列中的下一个图像，它将产生下一个点作为输出，然后在下一次迭代中成为输入点。

如果您想尝试跟踪不同的像素集（而不是cv::goodFeaturesToTrack或类似函数生成的特征），那么只需将这些像素提供给cv::calcOpticalFlowPyrLK以及下一张图像。

很简单，在代码中：

// Obtain first image and set up two feature vectors
cv::Mat image_prev, image_next;
std::vector<cv::Point> features_prev, features_next;

image_next = getImage();

// Obtain initial set of features
cv::goodFeaturesToTrack(image_next, // the image 
  features_next,   // the output detected features
  max_count,  // the maximum number of features 
  qlevel,     // quality level
  minDist     // min distance between two features
);

// Tracker is initialised and initial features are stored in features_next
// Now iterate through rest of images
for(;;)
{
    image_prev = image_next.clone();
    feature_prev = features_next;
    image_next = getImage();  // Get next image

    // Find position of feature in new image
    cv::calcOpticalFlowPyrLK(
      image_prev, image_next, // 2 consecutive images
      points_prev, // input point positions in first im
      points_next, // output point positions in the 2nd
      status,    // tracking success
      err      // tracking error
    );

    if ( stopTracking() ) break;
}

Answer 2

cv :: calcOpticalFlowPyrLK（..）函数使用参数：

cv :: calcOpticalFlowPyrLK（prev_gray，curr_gray，features_prev，features_next，status，err）;

cv::Mat prev_gray, curr_gray;
std::vector<cv::Point2f> features_prev, features_next;
std::vector<uchar> status;
std::vector<float> err;

在下一帧中查找像素的最简单（部分）代码：

features_prev.push_back(cv::Point(4, 5));
cv::calcOpticalFlowPyrLK(prev_gray, curr_gray, features_prev, features_next, status, err);

如果成功找到像素status[0] == 1，features_next[0]将在下一帧中显示像素的坐标。可以在此示例中找到值信息：OpenCV/samples/cpp/lkdemo.cpp

使用光流进行OpenCV跟踪

2 个答案: