OpenCV 4.10.0-dev
Open Source Computer Vision
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AKAZE and ORB planar tracking

Prev Tutorial: AKAZE local features matching
Next Tutorial: Basic concepts of the homography explained with code

Original author Fedor Morozov
Compatibility OpenCV >= 3.0

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:
    1. Detect and describe keypoints
    2. Match them using bruteforce matcher
    3. Estimate homography transformation using RANSAC
    4. Filter inliers from all the matches
    5. Apply homography transformation to the bounding box to find the object
    6. Draw bounding box and inliers, compute inlier ratio as evaluation metric

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.

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

./planar_tracking blais.mp4

Source Code

#include <opencv2/highgui.hpp> //for imshow
#include <vector>
#include <iostream>
#include <iomanip>
#include "stats.h" // Stats structure definition
#include "utils.h" // Drawing and printing functions
using namespace std;
using namespace cv;
const double akaze_thresh = 3e-4; // AKAZE detection threshold set to locate about 1000 keypoints
const double ransac_thresh = 2.5f; // RANSAC inlier threshold
const double nn_match_ratio = 0.8f; // Nearest-neighbour matching ratio
const int bb_min_inliers = 100; // Minimal number of inliers to draw bounding box
const int stats_update_period = 10; // On-screen statistics are updated every 10 frames
namespace example {
class Tracker
{
public:
detector(_detector),
matcher(_matcher)
{}
void setFirstFrame(const Mat frame, vector<Point2f> bb, string title, Stats& stats);
Mat process(const Mat frame, Stats& stats);
Ptr<Feature2D> getDetector() {
return detector;
}
protected:
Ptr<Feature2D> detector;
Mat first_frame, first_desc;
vector<KeyPoint> first_kp;
vector<Point2f> object_bb;
};
void Tracker::setFirstFrame(const Mat frame, vector<Point2f> bb, string title, Stats& stats)
{
cv::Point *ptMask = new cv::Point[bb.size()];
const Point* ptContain = { &ptMask[0] };
int iSize = static_cast<int>(bb.size());
for (size_t i=0; i<bb.size(); i++) {
ptMask[i].x = static_cast<int>(bb[i].x);
ptMask[i].y = static_cast<int>(bb[i].y);
}
first_frame = frame.clone();
cv::Mat matMask = cv::Mat::zeros(frame.size(), CV_8UC1);
cv::fillPoly(matMask, &ptContain, &iSize, 1, cv::Scalar::all(255));
detector->detectAndCompute(first_frame, matMask, 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;
delete[] ptMask;
}
Mat Tracker::process(const Mat frame, Stats& stats)
{
vector<KeyPoint> kp;
Mat desc;
tm.start();
detector->detectAndCompute(frame, noArray(), kp, desc);
stats.keypoints = (int)kp.size();
vector< vector<DMatch> > matches;
vector<KeyPoint> matched1, matched2;
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]);
}
}
stats.matches = (int)matched1.size();
Mat inlier_mask, homography;
vector<KeyPoint> inliers1, inliers2;
vector<DMatch> inlier_matches;
if(matched1.size() >= 4) {
homography = findHomography(Points(matched1), Points(matched2),
RANSAC, ransac_thresh, inlier_mask);
}
tm.stop();
stats.fps = 1. / tm.getTimeSec();
if(matched1.size() < 4 || homography.empty()) {
Mat res;
hconcat(first_frame, frame, res);
stats.inliers = 0;
stats.ratio = 0;
return res;
}
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));
}
}
stats.inliers = (int)inliers1.size();
stats.ratio = stats.inliers * 1.0 / stats.matches;
vector<Point2f> new_bb;
perspectiveTransform(object_bb, new_bb, homography);
Mat frame_with_bb = frame.clone();
if(stats.inliers >= bb_min_inliers) {
drawBoundingBox(frame_with_bb, new_bb);
}
Mat res;
drawMatches(first_frame, inliers1, frame_with_bb, inliers2,
inlier_matches, res,
Scalar(255, 0, 0), Scalar(255, 0, 0));
return res;
}
}
int main(int argc, char **argv)
{
CommandLineParser parser(argc, argv, "{@input_path |0|input path can be a camera id, like 0,1,2 or a video filename}");
parser.printMessage();
string input_path = parser.get<string>(0);
string video_name = input_path;
VideoCapture video_in;
if ( ( isdigit(input_path[0]) && input_path.size() == 1 ) )
{
int camera_no = input_path[0] - '0';
video_in.open( camera_no );
}
else {
video_in.open(video_name);
}
if(!video_in.isOpened()) {
cerr << "Couldn't open " << video_name << endl;
return 1;
}
Stats stats, akaze_stats, orb_stats;
Ptr<AKAZE> akaze = AKAZE::create();
akaze->setThreshold(akaze_thresh);
Ptr<ORB> orb = ORB::create();
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
example::Tracker akaze_tracker(akaze, matcher);
example::Tracker orb_tracker(orb, matcher);
Mat frame;
namedWindow(video_name, WINDOW_NORMAL);
cout << "\nPress any key to stop the video and select a bounding box" << endl;
while ( waitKey(1) < 1 )
{
video_in >> frame;
cv::resizeWindow(video_name, frame.size());
imshow(video_name, frame);
}
vector<Point2f> bb;
cv::Rect uBox = cv::selectROI(video_name, frame);
bb.push_back(cv::Point2f(static_cast<float>(uBox.x), static_cast<float>(uBox.y)));
bb.push_back(cv::Point2f(static_cast<float>(uBox.x+uBox.width), static_cast<float>(uBox.y)));
bb.push_back(cv::Point2f(static_cast<float>(uBox.x+uBox.width), static_cast<float>(uBox.y+uBox.height)));
bb.push_back(cv::Point2f(static_cast<float>(uBox.x), static_cast<float>(uBox.y+uBox.height)));
akaze_tracker.setFirstFrame(frame, bb, "AKAZE", stats);
orb_tracker.setFirstFrame(frame, bb, "ORB", stats);
Stats akaze_draw_stats, orb_draw_stats;
Mat akaze_res, orb_res, res_frame;
int i = 0;
for(;;) {
i++;
bool update_stats = (i % stats_update_period == 0);
video_in >> frame;
// stop the program if no more images
if(frame.empty()) break;
akaze_res = akaze_tracker.process(frame, stats);
akaze_stats += stats;
if(update_stats) {
akaze_draw_stats = stats;
}
orb->setMaxFeatures(stats.keypoints);
orb_res = orb_tracker.process(frame, stats);
orb_stats += stats;
if(update_stats) {
orb_draw_stats = stats;
}
drawStatistics(akaze_res, akaze_draw_stats);
drawStatistics(orb_res, orb_draw_stats);
vconcat(akaze_res, orb_res, res_frame);
cv::imshow(video_name, res_frame);
if(waitKey(1)==27) break; //quit on ESC button
}
akaze_stats /= i - 1;
orb_stats /= i - 1;
printStatistics("AKAZE", akaze_stats);
printStatistics("ORB", orb_stats);
return 0;
}
Designed for command line parsing.
Definition utility.hpp:890
Class for matching keypoint descriptors.
Definition types.hpp:849
n-dimensional dense array class
Definition mat.hpp:829
CV_NODISCARD_STD Mat clone() const
Creates a full copy of the array and the underlying data.
MatSize size
Definition mat.hpp:2177
static CV_NODISCARD_STD MatExpr zeros(int rows, int cols, int type)
Returns a zero array of the specified size and type.
_Tp & at(int i0=0)
Returns a reference to the specified array element.
bool empty() const
Returns true if the array has no elements.
_Tp y
y coordinate of the point
Definition types.hpp:202
_Tp x
x coordinate of the point
Definition types.hpp:201
Template class for 2D rectangles.
Definition types.hpp:444
_Tp x
x coordinate of the top-left corner
Definition types.hpp:487
_Tp y
y coordinate of the top-left corner
Definition types.hpp:488
_Tp width
width of the rectangle
Definition types.hpp:489
_Tp height
height of the rectangle
Definition types.hpp:490
static Scalar_< double > all(double v0)
a Class to measure passing time.
Definition utility.hpp:326
void start()
starts counting ticks.
Definition utility.hpp:335
double getTimeSec() const
returns passed time in seconds.
Definition utility.hpp:371
void stop()
stops counting ticks.
Definition utility.hpp:341
Base abstract class for the long-term tracker.
Definition tracking.hpp:726
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:747
virtual bool open(const String &filename, int apiPreference=CAP_ANY)
Opens a video file or a capturing device or an IP video stream for video capturing.
virtual bool isOpened() const
Returns true if video capturing has been initialized already.
Mat findHomography(InputArray srcPoints, InputArray dstPoints, int method=0, double ransacReprojThreshold=3, OutputArray mask=noArray(), const int maxIters=2000, const double confidence=0.995)
Finds a perspective transformation between two planes.
void vconcat(const Mat *src, size_t nsrc, OutputArray dst)
Applies vertical concatenation to given matrices.
void perspectiveTransform(InputArray src, OutputArray dst, InputArray m)
Performs the perspective matrix transformation of vectors.
void hconcat(const Mat *src, size_t nsrc, OutputArray dst)
Applies horizontal concatenation to given matrices.
std::shared_ptr< _Tp > Ptr
Definition cvstd_wrapper.hpp:23
InputOutputArray noArray()
Returns an empty InputArray or OutputArray.
unsigned char uchar
Definition interface.h:51
#define CV_8UC1
Definition interface.h:88
void drawMatches(InputArray img1, const std::vector< KeyPoint > &keypoints1, InputArray img2, const std::vector< KeyPoint > &keypoints2, const std::vector< DMatch > &matches1to2, InputOutputArray outImg, const Scalar &matchColor=Scalar::all(-1), const Scalar &singlePointColor=Scalar::all(-1), const std::vector< char > &matchesMask=std::vector< char >(), DrawMatchesFlags flags=DrawMatchesFlags::DEFAULT)
Draws the found matches of keypoints from two images.
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
int waitKey(int delay=0)
Waits for a pressed key.
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.
void resizeWindow(const String &winname, int width, int height)
Resizes the window to the specified size.
Rect selectROI(const String &windowName, InputArray img, bool showCrosshair=true, bool fromCenter=false, bool printNotice=true)
Allows users to select a ROI on the given image.
void fillPoly(InputOutputArray img, InputArrayOfArrays pts, const Scalar &color, int lineType=LINE_8, int shift=0, Point offset=Point())
Fills the area bounded by one or more polygons.
void putText(InputOutputArray img, const String &text, Point org, int fontFace, double fontScale, Scalar color, int thickness=1, int lineType=LINE_8, bool bottomLeftOrigin=false)
Draws a text string.
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
Definition core.hpp:107
STL namespace.

Explanation

Tracker class

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

  • Setting up the first frame

    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;
    }

    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
    1. Locate keypoints and compute descriptors

      (*detector)(frame, noArray(), kp, desc);

      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.

    2. Use 2-nn matcher to find correspondences
      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]);
      }
      }
      If the closest match is nn_match_ratio closer than the second closest one, then it's a match.
    3. Use RANSAC to estimate homography transformation
      homography = findHomography(Points(matched1), Points(matched2),
      RANSAC, ransac_thresh, inlier_mask);
      If there are at least 4 matches we can use random sample consensus to estimate image transformation.
    4. Save the inliers
      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));
      }
      }
      Since findHomography computes the inliers we only have to save the chosen points and matches.
    5. Project object bounding box

      perspectiveTransform(object_bb, new_bb, homography);

      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.

AKAZE statistics:

Matches 626
Inliers 410
Inlier ratio 0.58
Keypoints 1117

ORB statistics:

Matches 504
Inliers 319
Inlier ratio 0.56
Keypoints 1112