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I'm working on Opencv Project, I'm using C++. I am stuck at computing moving objects distance and speed.

Problem:

I have detected moving cars in video using Haar classifier in Opencv.
But didn't find any way to calculate moving objects
distance from camera as well as moving objects speed. 
I want to achieve this using only 1 Camera. I am not using Stereo vision.

Here is my code:

#include <stdio.h>
#include <opencv/cv.h>
#include <opencv/highgui.h>

CvHaarClassifierCascade *cascade;
CvMemStorage            *storage;

void detect(IplImage *img);

int main(int argc, char** argv)
{
  CvCapture *capture;
  IplImage  *frame;
  int input_resize_percent = 100;

  if(argc < 3)
  {
    std::cout << "Usage " << argv[0] << " cascade.xml video.avi" << std::endl;
    return 0;
  }

  if(argc == 4)
  {
    input_resize_percent = atoi(argv[3]);
    std::cout << "Resizing to: " << input_resize_percent << "%" << std::endl;
  }

  cascade = (CvHaarClassifierCascade*) cvLoad(argv[1], 0, 0, 0);
  storage = cvCreateMemStorage(0);
  capture = cvCaptureFromAVI(argv[2]);

  assert(cascade && storage && capture);

  cvNamedWindow("video", 1);

  IplImage* frame1 = cvQueryFrame(capture);
  frame = cvCreateImage(cvSize((int)((frame1->width*input_resize_percent)/100) , (int)((frame1->height*input_resize_percent)/100)), frame1->depth, frame1->nChannels);

  const int KEY_SPACE  = 32;
  const int KEY_ESC    = 27;

  int key = 0;
  do
  {
    frame1 = cvQueryFrame(capture);

    if(!frame1)
      break;

    cvResize(frame1, frame);

    detect(frame);

    key = cvWaitKey(10);

    if(key == KEY_SPACE)
      key = cvWaitKey(0);

    if(key == KEY_ESC)
      break;

  }while(1);

  cvDestroyAllWindows();
  cvReleaseImage(&frame);
  cvReleaseCapture(&capture);
  cvReleaseHaarClassifierCascade(&cascade);
  cvReleaseMemStorage(&storage);

  return 0;
}

void detect(IplImage *img)
{
  CvSize img_size = cvGetSize(img);
  CvSeq *object = cvHaarDetectObjects(
    img,
    cascade,
    storage,
    1.1, //1.1,//1.5, //-------------------SCALE FACTOR
    1, //2        //------------------MIN NEIGHBOURS
    0, //CV_HAAR_DO_CANNY_PRUNING
    cvSize(0,0),//cvSize( 30,30), // ------MINSIZE
    img_size //cvSize(70,70)//cvSize(640,480)  //---------MAXSIZE
    );

  std::cout << "Total: " << object->total << " cars" << std::endl;
  for(int i = 0 ; i < ( object ? object->total : 0 ) ; i++)
  {
    CvRect *r = (CvRect*)cvGetSeqElem(object, i);
    cvRectangle(img,
      cvPoint(r->x, r->y),
      cvPoint(r->x + r->width, r->y + r->height),
      CV_RGB(255, 0, 0), 2, 8, 0);
  }

  cvShowImage("video", img);
}

If you have any example please provide for better understanding. Its appreciated.

Thanks

Community
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Surinder ツ
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  • @RonakBhatt I have detected moving cars using haar classifier. What's wrong in my question. If it looks wrong you must edit not to downvote. – Surinder ツ Jan 20 '14 at 11:30
  • Put your coding here... So that we can understand whewre you are facig your problem... – The Hungry Dictator Jan 20 '14 at 11:31
  • @RonakBhatt I have posted my code there. – Surinder ツ Jan 20 '14 at 11:39
  • First of all have you tried to debug that code?? And if not then please debug it and find it where you are getting error?? – The Hungry Dictator Jan 20 '14 at 11:48
  • my question is how to compute distance and speed of object from camera? I am looking for the formula which can be used. Did you understand my problem yet or not? – Surinder ツ Jan 20 '14 at 11:52
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    if you have detect the cars, you already have the rectangle containing them. from the center of those rectangles, you have your pixel-wise dx and dv=dx/dt. If you want a real-world distance instead of pixels, you need to calibrate your camera, using opencv calibration API. – baci Jan 20 '14 at 21:09
  • From the size of the car (hopefully the cars you are interested in are all of similar sizes) you can estimate its distance, then from change of size and change of position you can estimates its speed. As @Canberk Baci says, you will have to do some camera calibration. – Bull Jan 21 '14 at 03:10
  • @CanberkBaci first thing is that, I am not gonna use Calibration. how will i calculate dx,dt. If you have good explanation with the code.Post a answer. – Surinder ツ Jan 21 '14 at 04:59
  • @B... Can you provide me some link having detail explanation to achieve that. – Surinder ツ Jan 21 '14 at 05:00
  • @Surinder, did you find any solution for your question? If yes, please share the solution here. – Sivaram Boina Feb 12 '19 at 02:56

2 Answers2

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I doubt about accuracy, but below mentioned way can help you to find distance of object(moving) to some extent.

  • Steps:
  • Find the suitable network to feed your frames for identifying the objects in the video.
  • Get coordinates of the detections and probably we could find length/area of the detection in further.
  • Before doing all this, find the focal length of the camera with an object of known size placed at known distance.(Refer Adrian pyimagesearch website).
  • With known focal length, and the moving object of known size, we can find the distance of the object while moving with the change in area of detected frame.
Sivaram Boina
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0

use this code I have done it in android

    static constexpr int32_t cam_number     = 1;                 /**< The number of the camera, the 0 is the built in my computer. */
    static constexpr int32_t cam_width      = 640;               /**< Width of the video's resolution. */
    static constexpr int32_t cam_height     = 480;               /**< Height of the video's resolution. */
    static constexpr int32_t threshold_min  = 245;               /**< Minimum value of the binary threshold. */
    static constexpr int32_t threshold_max  = 255;               /**< Maximum value of the binary threshold. */

Look-up table for linear interpolation. If you want to make your own version, you have to re-measure these values.

static std::vector<double> pixel  = {42.0, 94.0, 122.0, 139.0, 150.0, 157.0, 163.0, 168.0, 171.0};  /**< Measured values of pixels. */
static std::vector<double> cm     = {20.0, 30.0, 40.0,  50.0,  60.0,  70.0,  80.0,  90.0,  100.0};  /**< Measured values of centimeters. */

/* Initialize the video formats. */
cv::Mat video=srcImg;
cv::Mat video_gray=resultImage;
cv::Mat video_black_white;
    try {
        /* Vectors for contours and hierarchy. */
        std::vector<std::vector<cv::Point>> contours;
        std::vector<cv::Vec4i> hierarchy;

        /* Get a new frame from the camera, convert it to grayscale, then make into black&white with binary threshold. */

        cv::cvtColor(video, video_gray, cv::COLOR_RGB2GRAY);
        cv::threshold(video_gray, video_black_white, threshold_min, threshold_max,
                      cv::THRESH_BINARY);

        /* Get contours with full hierararchy and simple approximation. */
        cv::findContours(video_black_white, contours, hierarchy, cv::RETR_TREE,
                         cv::CHAIN_APPROX_SIMPLE, cv::Point(0, 0));

        /* If there are no contours, skip everything, otherwise there would be an exception. */
        if (contours.size()) {
            /* Get moments. */
            cv::Moments m = cv::moments(contours[0]);
            /* Protection from divison by zero. */
            if (m.m00 > 0.0) {
                /* Calculate the x,y coordinates of the laser point. */
                double coord_x = m.m10 / m.m00;
                double coord_y = m.m01 / m.m00;

                /* Make sure, that we are in the look-up table's range. */
                if ((coord_y > pixel[0]) && (coord_y < pixel[pixel.size() - 1])) {
                    /* Find the place of the coordinate in the look-up table. */
                    uint32_t i = 0;
                    while (coord_y > pixel[i + 1]) {
                        i++;
                    }
                    /* Calculate the value with linear interpolation. */
                    double distance = cm[i] + ((coord_y - pixel[i]) * (cm[i + 1] - cm[i]) /
                                               (pixel[i + 1] - pixel[i]));
                    dscmm=distance;


     std::cout << "X: " << coord_x << "\tY: " << coord_y << "\tDistance: "
//                                  << distance << "\n";


                    /* Draw a circle on the laser and put a text with the distance on it. */
                    cv::circle(video, cv::Point(coord_x, coord_y), 5, cv::Scalar(0, 0, 0), 1,
                               8);
                    cv::putText(video, std::to_string(distance), cv::Point(coord_x, coord_y),
                                cv::FONT_HERSHEY_SCRIPT_SIMPLEX, 0.5, cv::Scalar(255, 0, 0), 1);
                }
            }
        }
        /* Show the picture. */

        /* Press any key to exit. */

    }
        /* Write out if there is an error. */
    catch (std::exception &e) {
        LOGD( "JNI:", e.what());
    }
Angel F Syrus
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