OpenCV tracking using optical flow

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.

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.

Very simply, in code:

// 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
    image_prev = image_next.clone();
    feature_prev = features_next;
    image_next = getImage();  // Get next image

    // Find position of feature in new image
      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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