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POopencv- vehicle tracking using optical flow
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  1. POopencv- vehicle tracking using optical flow
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    copied!<p>I have implemented optical flow to track vehicles on road and it turned out to be very slow.</p> <p>my code uses the functions:</p> <ul> <li>cvGoodFeaturesToTrack</li> <li>cvFindCornerSubPix</li> <li>cvCalcOpticalFlowPyrLK</li> </ul> <p>How do I make this tracking fast and efficient?</p> <p>My code is:</p> <pre><code>#include "highgui.h" #include "cv.h" #include "cxcore.h" #include &lt;iostream&gt; using namespace std; const int MAX_CORNERS = 500; int main() { CvCapture* capture=cvCreateFileCapture("E:\cam1.avi"); IplImage* img_A;// = cvLoadImage("image0.png", CV_LOAD_IMAGE_GRAYSCALE); IplImage* img_B;// = cvLoadImage("image1.png", CV_LOAD_IMAGE_GRAYSCALE); img_A=cvQueryFrame(capture); IplImage* imgA = cvCreateImage( cvGetSize(img_A), 8, 1 ); IplImage* imgB = cvCreateImage( cvGetSize(img_A), 8, 1 ); cvNamedWindow( "ImageA", CV_WINDOW_AUTOSIZE ); cvNamedWindow( "ImageB", CV_WINDOW_AUTOSIZE ); cvNamedWindow( "LKpyr_OpticalFlow", CV_WINDOW_AUTOSIZE ); while(1) { int couter=0; for(int k=0;k&lt;20;k++) { img_B=cvQueryFrame(capture); } //cvCvtColor(imgA,imgA,CV_BGR2GRAY); //cvCvtColor(imgB,imgB,CV_BGR2GRAY); // Load two images and allocate other structures /*IplImage* imgA = cvLoadImage("image0.png", CV_LOAD_IMAGE_GRAYSCALE); IplImage* imgB = cvLoadImage("image1.png", CV_LOAD_IMAGE_GRAYSCALE);*/ CvSize img_sz = cvGetSize( img_A ); int win_size = 10; IplImage* imgC = cvCreateImage( cvGetSize(img_A), 8, 1 ); cvZero(imgC); // Get the features for tracking IplImage* eig_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 ); IplImage* tmp_image = cvCreateImage( img_sz, IPL_DEPTH_32F, 1 ); int corner_count = MAX_CORNERS; CvPoint2D32f* cornersA = new CvPoint2D32f[ MAX_CORNERS ]; cvCvtColor(img_A,imgA,CV_BGR2GRAY); cvCvtColor(img_B,imgB,CV_BGR2GRAY); cvGoodFeaturesToTrack( imgA, eig_image, tmp_image, cornersA, &amp;corner_count ,0.05, 5.0, 0, 3, 0, 0.04 ); cvFindCornerSubPix( imgA, cornersA, corner_count, cvSize( win_size, win_size ) ,cvSize( -1, -1 ), cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03 ) ); // Call Lucas Kanade algorithm char features_found[ MAX_CORNERS ]; float feature_errors[ MAX_CORNERS ]; CvSize pyr_sz = cvSize( imgA-&gt;width+8, imgB-&gt;height/3 ); IplImage* pyrA = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 ); IplImage* pyrB = cvCreateImage( pyr_sz, IPL_DEPTH_32F, 1 ); CvPoint2D32f* cornersB = new CvPoint2D32f[ MAX_CORNERS ]; /*int jk=0; for(int i=0;i&lt;imgA-&gt;width;i+=10) { for(int j=0;j&lt;imgA-&gt;height;j+=10) { cornersA[jk].x=i; cornersA[jk].y=j; ++jk; } } */ cvCalcOpticalFlowPyrLK( imgA, imgB, pyrA, pyrB, cornersA, cornersB, corner_count, cvSize( win_size, win_size ), 5, features_found, feature_errors, cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.3 ), 0 ); // Make an image of the results for( int i=0; i &lt; corner_count; i++ ) { if( features_found[i]==0|| feature_errors[i]&gt;550 ) { //printf("Error is %f/n",feature_errors[i]); continue; } //printf("Got it/n"); CvPoint p0 = cvPoint( cvRound( cornersA[i].x ), cvRound( cornersA[i].y ) ); CvPoint p1 = cvPoint( cvRound( cornersB[i].x ), cvRound( cornersB[i].y ) ); cvLine( imgC, p0, p1, CV_RGB(255,0,0), 2 ); cout&lt;&lt;p0.x&lt;&lt;" "&lt;&lt;p0.y&lt;&lt;endl; } cvShowImage( "LKpyr_OpticalFlow", imgC ); cvShowImage( "ImageA", imgA ); cvShowImage( "ImageB", imgB ); //cvCopyImage(imgB,imgA); delete[] cornersA; delete[] cornersB; cvWaitKey(33); } return 0; } </code></pre>
 

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