opencv/doc/tutorials/features2d/akaze_tracking/akaze_tracking.markdown

AKAZE and ORB planar tracking {#tutorial_akaze_tracking}
=============================

Introduction
------------

In this tutorial we will compare *AKAZE* and *ORB* local features using them to find matches between
video frames and track object movements.

The algorithm is as follows:

-   Detect and describe keypoints on the first frame, manually set object boundaries
-   For every next frame:
    -#  Detect and describe keypoints
    -#  Match them using bruteforce matcher
    -#  Estimate homography transformation using RANSAC
    -#  Filter inliers from all the matches
    -#  Apply homography transformation to the bounding box to find the object
    -#  Draw bounding box and inliers, compute inlier ratio as evaluation metric

![](images/frame.png)

Data
----

To do the tracking we need a video and object position on the first frame.

You can download our example video and data from
[here](https://docs.google.com/file/d/0B72G7D4snftJandBb0taLVJHMFk).

To run the code you have to specify input (camera id or video_file). Then, select a bounding box with the mouse, and press any key to start tracking
@code{.none}
./planar_tracking blais.mp4
@endcode

Source Code
-----------

@include cpp/tutorial_code/features2D/AKAZE_tracking/planar_tracking.cpp

Explanation
-----------

### Tracker class

This class implements algorithm described abobve using given feature detector and descriptor
matcher.

-   **Setting up the first frame**
    @code{.cpp}
    void Tracker::setFirstFrame(const Mat frame, vector<Point2f> bb, string title, Stats& stats)
    {
        first_frame = frame.clone();
        (*detector)(first_frame, noArray(), first_kp, first_desc);
        stats.keypoints = (int)first_kp.size();
        drawBoundingBox(first_frame, bb);
        putText(first_frame, title, Point(0, 60), FONT_HERSHEY_PLAIN, 5, Scalar::all(0), 4);
        object_bb = bb;
    }
    @endcode
    We compute and store keypoints and descriptors from the first frame and prepare it for the
    output.

    We need to save number of detected keypoints to make sure both detectors locate roughly the same
    number of those.

-   **Processing frames**

    -#  Locate keypoints and compute descriptors
        @code{.cpp}
        (*detector)(frame, noArray(), kp, desc);
        @endcode

        To find matches between frames we have to locate the keypoints first.

        In this tutorial detectors are set up to find about 1000 keypoints on each frame.

    -#  Use 2-nn matcher to find correspondences
        @code{.cpp}
        matcher->knnMatch(first_desc, desc, matches, 2);
        for(unsigned i = 0; i < matches.size(); i++) {
            if(matches[i][0].distance < nn_match_ratio * matches[i][1].distance) {
                matched1.push_back(first_kp[matches[i][0].queryIdx]);
                matched2.push_back(      kp[matches[i][0].trainIdx]);
            }
        }
        @endcode
        If the closest match is *nn_match_ratio* closer than the second closest one, then it's a
        match.

    -#  Use *RANSAC* to estimate homography transformation
        @code{.cpp}
        homography = findHomography(Points(matched1), Points(matched2),
                                    RANSAC, ransac_thresh, inlier_mask);
        @endcode
        If there are at least 4 matches we can use random sample consensus to estimate image
        transformation.

    -#  Save the inliers
        @code{.cpp}
        for(unsigned i = 0; i < matched1.size(); i++) {
            if(inlier_mask.at<uchar>(i)) {
                int new_i = static_cast<int>(inliers1.size());
                inliers1.push_back(matched1[i]);
                inliers2.push_back(matched2[i]);
                inlier_matches.push_back(DMatch(new_i, new_i, 0));
            }
        }
        @endcode
        Since *findHomography* computes the inliers we only have to save the chosen points and
        matches.

    -#  Project object bounding box
        @code{.cpp}
        perspectiveTransform(object_bb, new_bb, homography);
        @endcode

        If there is a reasonable number of inliers we can use estimated transformation to locate the
        object.

Results
-------

You can watch the resulting [video on youtube](http://www.youtube.com/watch?v=LWY-w8AGGhE).

*AKAZE* statistics:
@code{.none}
Matches      626
Inliers      410
Inlier ratio 0.58
Keypoints    1117
@endcode

*ORB* statistics:
@code{.none}
Matches      504
Inliers      319
Inlier ratio 0.56
Keypoints    1112
@endcode