使用光流的 OpenCV 跟踪

问题描述

我用它作为跟踪算法的基础.

I use this to functions as a base of my tracking algorithm.

    //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 将上一张图像的点向量作为输入，并返回下一张图像上的适当点.假设我在上一张图像上有随机像素 (x, y)，如何使用 OpenCV 光流函数计算该像素在下一张图像上的位置?

cv::calcOpticalFlowPyrLK takes vector of points from the previous image as input, and returns appropriate points on the next image. Suppose I have random pixel (x, y) on the previous image, how can I calculate position of this pixel on the next image using OpenCV optical flow function?

推荐答案

在您编写时，cv::goodFeaturesToTrack 将图像作为输入并生成它认为适合跟踪的点向量"".这些是根据它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.通常通过将第一张图像传递给 goodFeaturesToTrack 并获取一组要跟踪的特征来初始化跟踪器.然后可以将这些特征作为前一个点以及序列中的下一个图像传递给 cv::calcOpticalFlowPyrLK，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.

As you write, cv::goodFeaturesToTrack takes an image as input and produces a vector of points which it deems "good to track". These are chosen based on their ability to stand out from their surroundings, and are based on Harris corners in the image. A tracker would normally be initialised by passing the first image to goodFeaturesToTrack and obtaining a set of features to track. These features could then be passed to cv::calcOpticalFlowPyrLK as the previous points, along with the next image in the sequence and it will produce the next points as output, which then become input points in the next iteration.

如果您想尝试跟踪一组不同的像素(而不是由 cv::goodFeaturesToTrack 或类似函数生成的特征)，只需将这些提供给 cv::calcOpticalFlowPyrLK 连同下一张图片.

If you want to try to track a different set of pixels (rather than features generated by cv::goodFeaturesToTrack or a similar function), then simply provide these to cv::calcOpticalFlowPyrLK along with the next image.

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;
}

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