Question

为了这样的目的，我看到很多关于粒子滤波器的帖子，但没有人谈论这些步骤。大多数在线教程适用于涉及R，Theta运动的运动模型。

我想使用粒子滤镜来跟踪一个简单的黄色斑点。这是嘈杂的，因为它在水下，有时可能会被遮挡。我如何为此实现模型，以及对象的“移动”功能可能是什么？

Answer 1

您可以使用光流来检测移动方向。

我就是这样做的：

#include <stdio.h>
#include <cv.h>
#include <highgui.h>
#include <math.h>
static const double pi = 3.14159265358979323846;
inline static double square(int a)
{
return a * a;
}

inline static void allocateOnDemand( IplImage **img, CvSize size, int depth, int channels
 )
{
if ( *img != NULL ) return;
 *img = cvCreateImage( size, depth, channels );
if ( *img == NULL )
 {
 fprintf(stderr, "Error: Couldn't allocate image. Out of memory?\n");
 exit(-1);
 }
}
int main(void)
{

    CvCapture *input_video = cvCaptureFromCAM(0);
if (input_video == NULL)
 {

 fprintf(stderr, "Error: Can't open video.\n");
 return -1;
 }

 cvQueryFrame( input_video );

 CvSize frame_size;
 frame_size.height =
 (int) cvGetCaptureProperty( input_video, CV_CAP_PROP_FRAME_HEIGHT );
 frame_size.width =
 (int) cvGetCaptureProperty( input_video, CV_CAP_PROP_FRAME_WIDTH );

long number_of_frames;

 cvSetCaptureProperty( input_video, CV_CAP_PROP_POS_AVI_RATIO, 1. );

 number_of_frames = (int) cvGetCaptureProperty( input_video, CV_CAP_PROP_POS_FRAMES );

 cvSetCaptureProperty( input_video, CV_CAP_PROP_POS_FRAMES, 0. );

 cvNamedWindow("Optical Flow", CV_WINDOW_AUTOSIZE);
long current_frame = 0;
while(true)
 {
 static IplImage *frame = NULL, *frame1 = NULL, *frame1_1C = NULL, *frame2_1C =
NULL, *eig_image = NULL, *temp_image = NULL, *pyramid1 = NULL, *pyramid2 = NULL;


 cvSetCaptureProperty( input_video, CV_CAP_PROP_POS_FRAMES, current_frame );


 frame = cvQueryFrame( input_video );
 if (frame == NULL)
 {
 fprintf(stderr, "Error: Hmm. The end came sooner than we thought.\n");
 return -1;
 }


 allocateOnDemand( &frame1_1C, frame_size, IPL_DEPTH_8U, 1 );


 cvConvertImage(frame, frame1_1C, CV_CVTIMG_FLIP);


 allocateOnDemand( &frame1, frame_size, IPL_DEPTH_8U, 3 );
 cvConvertImage(frame, frame1, CV_CVTIMG_FLIP);


 frame = cvQueryFrame( input_video );
 if (frame == NULL)
 {
 fprintf(stderr, "Error: Hmm. The end came sooner than we thought.\n");
 return -1;
 }
 allocateOnDemand( &frame2_1C, frame_size, IPL_DEPTH_8U, 1 );
 cvConvertImage(frame, frame2_1C, CV_CVTIMG_FLIP);


 allocateOnDemand( &eig_image, frame_size, IPL_DEPTH_32F, 1 );
 allocateOnDemand( &temp_image, frame_size, IPL_DEPTH_32F, 1 );


 CvPoint2D32f frame1_features[400];


 int number_of_features;


 number_of_features = 400;


 cvGoodFeaturesToTrack(frame1_1C, eig_image, temp_image, frame1_features, &
number_of_features, .01, .01, NULL);

 CvPoint2D32f frame2_features[400];


 char optical_flow_found_feature[400];

 float optical_flow_feature_error[400];

 CvSize optical_flow_window = cvSize(3,3);



 CvTermCriteria optical_flow_termination_criteria
 = cvTermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, .3 );


 allocateOnDemand( &pyramid1, frame_size, IPL_DEPTH_8U, 1 );
 allocateOnDemand( &pyramid2, frame_size, IPL_DEPTH_8U, 1 );


 cvCalcOpticalFlowPyrLK(frame1_1C, frame2_1C, pyramid1, pyramid2, frame1_features,
frame2_features, number_of_features, optical_flow_window, 5,
optical_flow_found_feature, optical_flow_feature_error,
optical_flow_termination_criteria, 0 );


 for(int i = 0; i < number_of_features; i++)
 {

 if ( optical_flow_found_feature[i] == 0 ) continue;
 int line_thickness; line_thickness = 1;

 CvScalar line_color; line_color = CV_RGB(255,0,0);


 CvPoint p,q;
 p.x = (int) frame1_features[i].x;
 p.y = (int) frame1_features[i].y;
 q.x = (int) frame2_features[i].x;
 q.y = (int) frame2_features[i].y;
 double angle; angle = atan2( (double) p.y - q.y, (double) p.x - q.x );
 double hypotenuse; hypotenuse = sqrt( square(p.y - q.y) + square(p.x - q.x) );

 q.x = (int) (p.x - 3 * hypotenuse * cos(angle));
 q.y = (int) (p.y - 3 * hypotenuse * sin(angle));


 cvLine( frame1, p, q, line_color, line_thickness, CV_AA, 0 );

 p.x = (int) (q.x + 9 * cos(angle + pi / 4));
 p.y = (int) (q.y + 9 * sin(angle + pi / 4));
 cvLine( frame1, p, q, line_color, line_thickness, CV_AA, 0 );
 p.x = (int) (q.x + 9 * cos(angle - pi / 4));
 p.y = (int) (q.y + 9 * sin(angle - pi / 4));
 cvLine( frame1, p, q, line_color, line_thickness, CV_AA, 0 );
 }

 cvShowImage("Optical Flow", frame1);

 int key_pressed;
 key_pressed = cvWaitKey(0);

 if (key_pressed == 'b' || key_pressed == 'B') current_frame--;
 else current_frame++;

 if (current_frame < 0) current_frame = 0;
 if (current_frame >= number_of_frames - 1) current_frame = number_of_frames - 2;
 }
 }

计算机视觉跟踪的粒子滤波模型

1 个答案: