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Сделал программу получения видео в камеры и поиска на видео заданного объекта. По образцу с оф. сайта openCV. int main( int argc, char** argv ) { VideoCapture cap(1); // Открыть камеру (устройство 1). Для открытия встроенной камеры вызывать 0 устройство. if(!cap.isOpened()) // Проверка корректности отработки { string message = "Проверьте камеру или укажите другой номер устройства в коде"; // readme(message); return -1; } //Mat img_object = imread("D:\\005.jpg", CV_LOAD_IMAGE_GRAYSCALE); Mat img_object = imread("D:\\feat\\iiL2.jpg", CV_LOAD_IMAGE_GRAYSCALE ); for(;; ) { Mat frame; cap >> frame; // Получить очередной фрейм из камеры Mat img_scene = frame; // Mat img_scene = imread("D:\\feat\\iiR.jpg", CV_LOAD_IMAGE_GRAYSCALE ); // Mat img_object = imread("D:\\stereoImg\\iL.jpg", CV_LOAD_IMAGE_GRAYSCALE ); // Mat img_scene = imread("D:\\stereoImg\\iR.jpg", CV_LOAD_IMAGE_GRAYSCALE ); if( !img_object.data || !img_scene.data ) { std::cout<< " --(!) Error reading images " << std::endl; return -1; } // Detect the keypoints using SURF Detector int minHessian = 400; SurfFeatureDetector detector( minHessian ); std::vector<KeyPoint> keypoints_object, keypoints_scene; detector.detect( img_object, keypoints_object ); detector.detect( img_scene, keypoints_scene ); //-- Step 2: Calculate descriptors (feature vectors) SurfDescriptorExtractor extractor; Mat descriptors_object, descriptors_scene; extractor.compute( img_object, keypoints_object, descriptors_object ); extractor.compute( img_scene, keypoints_scene, descriptors_scene ); //-- Step 3: Matching descriptor vectors using FLANN matcher FlannBasedMatcher matcher; std::vector< DMatch > matches; matcher.match( descriptors_object, descriptors_scene, matches ); double max_dist = 0; double min_dist = 100; // Quick calculation of max and min distances between keypoints for( int i = 0; i < descriptors_object.rows; i++ ) { double dist = matches[i].distance; if( dist < min_dist ) min_dist = dist; if( dist > max_dist ) max_dist = dist; } printf("-- Max dist : %f \n", max_dist ); printf("-- Min dist : %f \n", min_dist ); //-- Draw only "good" matches (i.e. whose distance is less than 3 * min_dist ) std::vector< DMatch > good_matches; for( int i = 0; i < descriptors_object.rows; i++ ) { if( matches[i].distance < 3 * min_dist ) { good_matches.push_back( matches[i]); } } Mat img_matches; drawMatches( img_object, keypoints_object, img_scene, keypoints_scene, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); //-- Localize the object std::vector<Point2f> obj; std::vector<Point2f> scene; for(int i = 0; i < good_matches.size(); i++) { // Get the keypoints from the good matches // if( (keypoints_object[good_matches[i].queryIdx].pt.x - keypoints_object[good_matches[i].queryIdx].pt.x) > 0) { obj.push_back( keypoints_object[good_matches[i].queryIdx].pt ); scene.push_back( keypoints_scene[good_matches[i].trainIdx].pt ); /* printf("p1.x = %f\tp2.x = %f\tdx = %f\r\n", keypoints_object[good_matches[i].queryIdx].pt.x, keypoints_scene[ good_matches[i].trainIdx ].pt.x, keypoints_object[good_matches[i].queryIdx].pt.x - keypoints_scene[ good_matches[i].trainIdx ].pt.x); */ } } Mat H = findHomography( obj, scene, CV_RANSAC ); //-- Get the corners from the image_1 ( the object to be "detected" ) std::vector<Point2f> obj_corners(4); obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 ); obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows ); std::vector<Point2f> scene_corners(4); perspectiveTransform( obj_corners, scene_corners, H); //-- Draw lines between the corners (the mapped object in the scene - image_2 ) line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 ); line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 ); line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 ); line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 ); //-- Show detected matches imshow( "Good Matches & Object detection", img_matches ); H.release(); img_matches.release(); waitKey(20); } return 0; } Постоянно "вылетает" по memory exception. Я думаю из-за того что постоянно выделяю ресурсы и не освобождаю их. Скажите, что надо освобождать (release), а что должно само освобождаться?