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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.

Warning
  You need the OpenCV contrib module xfeatures2d to be able to use the AKAZE features.

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 <vector>
#include <iostream>
#include <iomanip>
#ifdef HAVE_OPENCV_XFEATURES2D
#include <opencv2/features.hpp>
#include "opencv2/xfeatures2d.hpp"
#include <opencv2/videoio.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/3d.hpp>
#include <opencv2/highgui.hpp> //for imshow
#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:
Tracker(Ptr<Feature2D> _detector, Ptr<DescriptorMatcher> _matcher) :
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;
Ptr<DescriptorMatcher> matcher;
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)
{
TickMeter tm;
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<xfeatures2d::AKAZE> akaze = xfeatures2d::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;
}
#else
int main()
{
std::cout << "This tutorial code needs the xfeatures2d contrib module to be run." << std::endl;
return 0;
}
#endif
3d.hpp
cv::CommandLineParser
Designed for command line parsing.
Definition utility.hpp:890
cv::DMatch
Class for matching keypoint descriptors.
Definition types.hpp:852
cv::Mat
n-dimensional dense array class
Definition mat.hpp:951
cv::Mat::clone
CV_NODISCARD_STD Mat clone() const
Creates a full copy of the array and the underlying data.
cv::Mat::size
MatSize size
Definition mat.hpp:2448
cv::Mat::zeros
static CV_NODISCARD_STD MatExpr zeros(int rows, int cols, int type)
Returns a zero array of the specified size and type.
cv::Mat::at
_Tp & at(int i0=0)
Returns a reference to the specified array element.
cv::Mat::empty
bool empty() const
Returns true if the array has no elements.
cv::Point_< int >
cv::Point_::y
_Tp y
y coordinate of the point
Definition types.hpp:202
cv::Point_::x
_Tp x
x coordinate of the point
Definition types.hpp:201
cv::Rect_
Template class for 2D rectangles.
Definition types.hpp:447
cv::Rect_::x
_Tp x
x coordinate of the top-left corner
Definition types.hpp:490
cv::Rect_::y
_Tp y
y coordinate of the top-left corner
Definition types.hpp:491
cv::Rect_::width
_Tp width
width of the rectangle
Definition types.hpp:492
cv::Rect_::height
_Tp height
height of the rectangle
Definition types.hpp:493
cv::Scalar_< double >
cv::Scalar_< double >::all
static Scalar_< double > all(double v0)
cv::TickMeter
a Class to measure passing time.
Definition utility.hpp:326
cv::TickMeter::start
void start()
starts counting ticks.
Definition utility.hpp:335
cv::TickMeter::getTimeSec
double getTimeSec() const
returns passed time in seconds.
Definition utility.hpp:371
cv::TickMeter::stop
void stop()
stops counting ticks.
Definition utility.hpp:341
cv::Tracker
Base abstract class for the long-term tracker.
Definition tracking.hpp:748
cv::VideoCapture
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:727
cv::VideoCapture::open
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.
cv::VideoCapture::isOpened
virtual bool isOpened() const
Returns true if video capturing has been initialized already.
features.hpp
cv::findHomography
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.
cv::vconcat
void vconcat(const Mat *src, size_t nsrc, OutputArray dst)
Applies vertical concatenation to given matrices.
cv::perspectiveTransform
void perspectiveTransform(InputArray src, OutputArray dst, InputArray m)
Performs the perspective matrix transformation of vectors.
cv::hconcat
void hconcat(const Mat *src, size_t nsrc, OutputArray dst)
Applies horizontal concatenation to given matrices.
cv::Ptr
std::shared_ptr< _Tp > Ptr
Definition cvstd_wrapper.hpp:23
cv::noArray
InputOutputArray noArray()
Returns an empty InputArray or OutputArray.
uchar
unsigned char uchar
Definition interface.h:51
CV_8UC1
#define CV_8UC1
Definition interface.h:99
cv::drawMatches
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.
cv::imshow
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.
cv::namedWindow
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.
cv::resizeWindow
void resizeWindow(const String &winname, int width, int height)
Resizes the window to the specified size.
cv::selectROI
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.
cv::fillPoly
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.
cv::putText
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.
highgui.hpp
main
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
imgproc.hpp
cv
Definition core.hpp:107
std
STL namespace.
videoio.hpp
xfeatures2d.hpp

# 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