An OpenCv-based moving object detection algorithm based on 1 implementation can be used to detect pedestrians or other moving objects.

#include <stdio.h>
#include <cv.h>
#include <cxcore.h>
#include <highgui.h>
int main( int argc, char** argv )

 // The statement IplImage Pointer to the
 IplImage* pFrame = NULL;
 IplImage* pFrImg = NULL;
 IplImage* pBkImg = NULL;
 CvMat* pFrameMat = NULL;
 CvMat* pFrMat = NULL;
 CvMat* pBkMat = NULL;

 CvCapture* pCapture = NULL;

 int nFrmNum = 0;
 // Create a window
 cvNamedWindow("video", 1);
 cvNamedWindow("background",1);
 cvNamedWindow("foreground",1);
 // Arrange the Windows in order
 cvMoveWindow("video", 30, 0);
 cvMoveWindow("background", 360, 0);
 cvMoveWindow("foreground", 690, 0);
 argc = 1;

 if( argc > 2 )
  {
   fprintf(stderr, "Usage: bkgrd [video_file_name]\n");
   return -1;
  }
 // Turn on the camera
 if (argc ==1)
  if( !(pCapture = cvCaptureFromCAM(-1)))
   {
  fprintf(stderr, "Can not open camera.\n");
  return -2;
   }
 // Open the video file
 if(argc == 2)
  if( !(pCapture = cvCaptureFromFile(argv[1])))
   {
  fprintf(stderr, "Can not open video file %s\n", argv[1]);
  return -2;
   }

 // The video is read frame by frame
 while(pFrame = cvQueryFrame( pCapture ))
  {
   nFrmNum++;

   // If it is the first 1 Frame, need to apply memory, and initialize
   if(nFrmNum == 1)
  {
   pBkImg = cvCreateImage(cvSize(pFrame->width, pFrame->height), IPL_DEPTH_8U,1);
   pFrImg = cvCreateImage(cvSize(pFrame->width, pFrame->height), IPL_DEPTH_8U,1);
   pBkMat  = cvCreateMat(pFrame->height, pFrame->width, CV_32FC1);
   pFrMat  = cvCreateMat(pFrame->height, pFrame->width, CV_32FC1);
   pFrameMat = cvCreateMat(pFrame->height, pFrame->width, CV_32FC1);
   // Convert to single channel image for reprocessing
   cvCvtColor(pFrame, pBkImg, CV_BGR2GRAY);
   cvCvtColor(pFrame, pFrImg, CV_BGR2GRAY);
   cvConvert(pFrImg, pFrameMat);
   cvConvert(pFrImg, pFrMat);
   cvConvert(pFrImg, pBkMat);
  }
   else
  {
   cvCvtColor(pFrame, pFrImg, CV_BGR2GRAY);
   cvConvert(pFrImg, pFrameMat);
   // A gaussian filter is used to smooth the image
   //cvSmooth(pFrameMat, pFrameMat, CV_GAUSSIAN, 3, 0, 0);

   // Subtract the current frame from the background
   cvAbsDiff(pFrameMat, pBkMat, pFrMat);
   //2 Valuate the foreground diagram
   cvThreshold(pFrMat, pFrImg, 60, 255.0, CV_THRESH_BINARY);
   // Morphological filtering was performed to remove the noise
   //cvErode(pFrImg, pFrImg, 0, 1);
   //cvDilate(pFrImg, pFrImg, 0, 1);
   // Update background
   cvRunningAvg(pFrameMat, pBkMat, 0.003, 0);
   // Converts the background to an image format for display
   cvConvert(pBkMat, pBkImg);
   // According to the image
   cvShowImage("video", pFrame);
   cvShowImage("background", pBkImg);
   cvShowImage("foreground", pFrImg);
   // If there is a key event, the loop is broken
   // This wait is also cvShowImage The function provides the time to complete the display
   // Wait time can be based on CPU Speed adjustment
   if( cvWaitKey(2) >= 0 )
    break;

  }
  }

 // Destruction of the window
 cvDestroyWindow("video");
 cvDestroyWindow("background");
 cvDestroyWindow("foreground");
 // Release image and matrix
 cvReleaseImage(&pFrImg);
 cvReleaseImage(&pBkImg);
 cvReleaseMat(&pFrameMat);
 cvReleaseMat(&pFrMat);
 cvReleaseMat(&pBkMat);
 cvReleaseCapture(&pCapture);
 return 0;
}