Uploaded byDaesik Jang
Open cv를 활용한 tracking
1. The document discusses three methods for object tracking in OpenCV: LK optical flow matching, SURF feature matching, and Kalman filtering. 2. It explains how to extract and track SURF features between frames and match features using BruteForce matching. 3. Kalman filtering is discussed as a way to predict and correct object positions frame to frame based on measurements and system dynamics.
Open cv를 활용한 tracking
- 1. LK matching SURF matching Kalman Filter OpenCV를 활용한 추적 특징점 기반 추적 Daesik Jang dsjang@kunsan.ac.kr 1
- 2. 1. LK 기반의 특징점 매칭 • goodFeaturesToTrack : 특징점 추출 • calcOpBcalFlowPyrLK : LK 알고리즘을 기반으로 주어진 feature-‐ prev 특징점들을 image_next에서 찾아 features_next에 저장 // Tracker is initialised and initial features are stored in features_next // Now iterate through rest of images // Obtain first image and set up two feature vectors for(;;) cv::Mat image_prev, image_next; { std::vector<cv::Point> features_prev, features_next; image_prev = image_next.clone(); feature_prev = features_next; image_next = getImage(); image_next = getImage(); // Get next image // Obtain initial set of features // Find position of feature in new image cv::goodFeaturesToTrack(image_next, // the image cv::calcOpticalFlowPyrLK( features_next, // the output detected features image_prev, image_next, // 2 consecutive images max_count, // the maximum number of features feature_prev, // input point positions in first im qlevel, // quality level features_next, // output point positions in the 2nd minDist // min distance between two features status, // tracking success ); err // tracking error ); if ( stopTracking() ) break; } 2
- 3. 2. SURF 특징을 이용한 매칭 • SurfFeatureDetector 클래스와 detect() 메소드를 이용한 SURF 특징 추출 – 추출된 특징은 keypoints 변수에 저장됨 // vector of keypoints std::vector<cv::KeyPoint> keypoints; // Construct the SURF feature detector object cv::SurfFeatureDetector surf( 2500.); // threshold // Detect the SURF features surf.detect(image,keypoints); // Draw the keypoints with scale and orientation information cv::drawKeypoints(image, // original image keypoints, // vector of keypoints featureImage, // the resulting image cv::Scalar(255,255,255), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS); //flag 3
- 4. 2. SURF 특징을 이용한 매칭 4
- 5. 2. SURF 특징을 이용한 매칭 • SurfDescriptorExtractor 클래스를 이용하여 keypoint로부터 특징값을 추출하여 descriptor에 저장 // Construction of the SURF descriptor extractor cv::SurfDescriptorExtractor surfDesc; // Extraction of the SURF descriptors cv::Mat descriptors1; surfDesc.compute(image1,keypoints1,descriptors1); 5
- 6. 2. SURF 특징을 이용한 매칭 • BruteForceMatcher 클래스를 이용하여 descriptor들 사이의 매칭 결과를 matches에 저장한다. // Construction of the matcher cv::BruteForceMatcher<cv::L2<float>> matcher; // Match the two image descriptors std::vector<cv::DMatch> matches; matcher.match(descriptors1,descriptors2, matches); 6
- 7. 2. SURF 특징을 이용한 매칭 • keypoint들과 matching 결과를 선으로 연결하여 그림으로 그린다. cv::Mat imageMatches; cv::drawMatches( image1,keypoints1, // 1st image and its keypoints image2,keypoints2, // 2nd image and its keypoints matches, // the matches imageMatches, // the image produced cv::Scalar(255,255,255)); // color of the lines 7
- 8. 2. SURF 특징을 이용한 매칭 • SURF 특징들의 매칭 결과 8
- 9. 3. OpenCV의 Kalman Filter를 이용한 예측 • KalmanFilter 객체의 시스템 설정 KalmanFilter KF(4, 2, 0); KF.transiBonMatrix = *(Mat_<float>(4, 4) << 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1); Mat_<float> measurement(2,1); measurement.setTo(Scalar(0)); // init... KF.statePre.at<float>(0) = mouse_info.x; KF.statePre.at<float>(1) = mouse_info.y; KF.statePre.at<float>(2) = 0; KF.statePre.at<float>(3) = 0; setIdenBty(KF.measurementMatrix); setIdenBty(KF.processNoiseCov, Scalar::all(1e-‐4)); setIdenBty(KF.measurementNoiseCov, Scalar::all(1e-‐1)); setIdenBty(KF.errorCovPost, Scalar::all(.1)); 9
- 10. 3. OpenCV의 Kalman Filter를 이용한 예측 • KF 객체를 이용한 예측과 수정 // First predict, to update the internal statePre variable Mat prediction = KF.predict(); Point predictPt(prediction.at<float>(0),prediction.at<float>(1)); // Get mouse point measurement(0) = mouse_info.x; measurement(1) = mouse_info.y; Point measPt(measurement(0),measurement(1)); // The "correct" phase that is going to use the predicted value and our measurement Mat estimated = KF.correct(measurement); Point statePt(estimated.at<float>(0),estimated.at<float>(1)); 10