AKAZE and ORB planar 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
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/features2d.hpp> #include <opencv2/videoio.hpp> #include <opencv2/opencv.hpp> #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: 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) { vector<KeyPoint> kp; Mat desc; 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); } 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) { if(argc < 2) { cerr << "Usage: " << endl << "akaze_track input_path" << endl << " (input_path can be a camera id, like 0,1,2 or a video filename)" << endl; return 1; } std::string video_name = argv[1]; std::stringstream ssFormat; ssFormat << atoi(argv[1]); VideoCapture video_in; if (video_name.compare(ssFormat.str())==0) { //test str==str(num) video_in.open(atoi(argv[1])); } else { video_in.open(video_name); } if(!video_in.isOpened()) { cerr << "Couldn't open " << argv[1] << 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; video_in >> frame; namedWindow(video_name, WINDOW_NORMAL); cv::resizeWindow(video_name, frame.cols, frame.rows); cout << "Please select a bounding box, and press any key to continue." << endl; 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; }
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
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.
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.
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.
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.
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