Hog+Camshift的人體跟蹤

這幾天跑了Opencv中的camshift演算法，發現目標需要自己去選，夠費勁的。

突然想要，是hog進行人體檢測，然後用shift去跟蹤是不是效果會好些。

camshift的跟蹤還好，主要是hog的檢測，opencv中hog檢測的誤檢率還是很高的。

程式我直接拿來了opencv中現成的代碼，也就是個簡單的demo。大家看看，感覺下效果就可以了

如果需要更精確的效果，程式還需要很多最佳化，畢竟opencv中的camshift演算法緊緊是重心的跟蹤迭代。

程式如下：

#include <fstream>#include <string>#include <cv.h>#include <highgui.h>#include <ml.h>#include <iostream>#include <fstream>#include <string>#include <vector>#include "cvaux.h"#include <iostream>#include <stdio.h>#include <string.h>#include <ctype.h>using namespace cv;using namespace std;Rect r ;int track_object = 0;Rect ObjectDectd(IplImage* frame,int object,Rect r);IplImage* MeanSift(IplImage *frame,Rect r);int main(){int number = 0;CvCapture* capture = 0;capture = cvCaptureFromAVI("112218.avi");if( !capture )    {        fprintf(stderr,"Could not initialize capturing...\n");        return -1;    }cvNamedWindow( "HogSiftDemo", 1 );for(;;){cout<<number<<endl;number++;IplImage* frame = 0;        frame = cvQueryFrame( capture );//frame = cvLoadImage("D:\\My Documents\\Visual Studio 2008\\Projects\\hogmeansift\\Debug\\crop001009.png");if(track_object == 0){r = ObjectDectd(frame,track_object,r);if(r.x!=0)track_object  = -1;}else frame = MeanSift(frame,r);//cvRectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3);cvShowImage("HogSiftDemo", frame);waitKey(1);}cvReleaseCapture( &capture );    cvDestroyWindow("HogSiftDemo");return 0;}Rect ObjectDectd(IplImage* frame,int object,Rect r){HOGDescriptor hog;hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());Mat img;img = frame;fflush(stdout);vector<Rect> found, found_filtered;double t = (double)getTickCount();int can = img.channels();hog.detectMultiScale(img, found, 0, Size(8,8), Size(32,32), 1.05, 2);t = (double)getTickCount() - t;printf("tdetection time = %gms\n", t*1000./cv::getTickFrequency());size_t i, j;if(found.size()!=0){//object = 1;for( i = 0; i < found.size(); i++ ){r = found[i];for( j = 0; j < found.size(); j++ )if( j != i && (r & found[j]) == r)break;if( j == found.size() )found_filtered.push_back(r);}for( i = 0; i < found_filtered.size(); i++ ){r = found_filtered[i];r.x += cvRound(r.width*0.1);r.width = cvRound(r.width*1);r.y += cvRound(r.height*0.07);r.height = cvRound(r.height*0.8);//cvRectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3);}}return r;}IplImage *image = 0, *hsv = 0, *hue = 0, *mask = 0, *backproject = 0, *histimg = 0;CvHistogram *hist = 0;int backproject_mode = 0;int select_object = 0;//int track_object = 0;int show_hist = 1;CvPoint origin;CvRect selection;CvRect track_window;CvBox2D track_box;CvConnectedComp track_comp;int hdims = 16;float hranges_arr[] = {0,180};float* hranges = hranges_arr;int vmin = 10, vmax = 256, smin = 30;int i, bin_w, c;CvScalar hsv2rgb( float hue ){    int rgb[3], p, sector;    static const int sector_data[][3]=        {{0,2,1}, {1,2,0}, {1,0,2}, {2,0,1}, {2,1,0}, {0,1,2}};    hue *= 0.033333333333333333333333333333333f;    sector = cvFloor(hue);    p = cvRound(255*(hue - sector));    p ^= sector & 1 ? 255 : 0;    rgb[sector_data[sector][0]] = 255;    rgb[sector_data[sector][1]] = 0;    rgb[sector_data[sector][2]] = p;    return cvScalar(rgb[2], rgb[1], rgb[0],0);}IplImage* MeanSift(IplImage *frame,Rect r){if( !image )    {        /* allocate all the buffers */        image = cvCreateImage( cvGetSize(frame), 8, 3 );        image->origin = frame->origin;        hsv = cvCreateImage( cvGetSize(frame), 8, 3 );        hue = cvCreateImage( cvGetSize(frame), 8, 1 );        mask = cvCreateImage( cvGetSize(frame), 8, 1 );        backproject = cvCreateImage( cvGetSize(frame), 8, 1 );        hist = cvCreateHist( 1, &hdims, CV_HIST_ARRAY, &hranges, 1 );       // histimg = cvCreateImage( cvSize(320,200), 8, 3 );        //cvZero( histimg );    }    cvCopy( frame, image, 0 );    cvCvtColor( image, hsv, CV_BGR2HSV );    if( track_object )    {        int _vmin = vmin, _vmax = vmax;        cvInRangeS( hsv, cvScalar(0,smin,MIN(_vmin,_vmax),0),                    cvScalar(180,256,MAX(_vmin,_vmax),0), mask );        cvSplit( hsv, hue, 0, 0, 0 );        if( track_object < 0 )        {selection.height = r.height;selection.width = r.width;selection.x = r.x;selection.y = r.y;           float max_val = 0.f;            cvSetImageROI( hue, selection );            cvSetImageROI( mask, selection );            cvCalcHist( &hue, hist, 0, mask );            cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 );            cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 );            cvResetImageROI( hue );            cvResetImageROI( mask );            track_window = selection;            track_object = 1;        }        cvCalcBackProject( &hue, backproject, hist );        cvAnd( backproject, mask, backproject, 0 );        cvCamShift( backproject, track_window,                    cvTermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 ),                    &track_comp, &track_box );        track_window = track_comp.rect;        if( backproject_mode )            cvCvtColor( backproject, image, CV_GRAY2BGR );        if( !image->origin )            track_box.angle = -track_box.angle;        //cvEllipseBox( image, track_box, CV_RGB(255,0,0), 3, CV_AA, 0 );Rect r;r.x = track_comp.rect.x;r.width = track_comp.rect.height;r.y = track_comp.rect.y;r.height = track_comp.rect.width;cvRectangle(image, r.tl(), r.br(), cv::Scalar(0,255,0), 3);    }    if( select_object && selection.width > 0 && selection.height > 0 )    {        cvSetImageROI( image, selection );        cvXorS( image, cvScalarAll(255), image, 0 );        cvResetImageROI( image );    }return image;}