Last page update: 19/10/2016

Last version: 1.0.0 (see Release Notes for more info)

Hi everyone,

There are several ways to perform vehicle detection, tracking and counting. Here is a step-by-step of a simplest way to do this:

1. First, you will need to detect the moving objects. An easy way to do vehicle detection is by using a Background Subtraction (BS) algorithm. You can try to use a background subtraction library like BGSLibrary.
2. For vehicle tracking, you will need to use a tracking algorithm. A simplest way to do this is by using a blob tracker algorithm (see cvBlob or OpenCVBlobsLib). So, send the foreground mask to cvBlob or OpenCVBlobsLib. For example, the cvBlob library provide some methods to get the centroid, the track and the ID of the moving objects. You can also set to draw a bounding box, the centroid and the angle of the tracked object.
3. And then, check if the centroid of the moving object has crossed a region of interest (i.e. virtual line) in your video.
4. Voilà! enjoy it :)

If you use this code for your publications, please cite it as:

@ONLINE{vdtc,
    author = "Andrews Sobral",
    title  = "Vehicle Detection, Tracking and Counting",
    year   = "2014",
    url    = "https://github.com/andrewssobral/simple_vehicle_counting"
}

• There is a Visual Studio 2013 template project in the vs2013/ folder. Open it in the Visual Studio IDE and select [Release]-[Win32] mode.
• The include files for the OpenCV 2.4.10 are provided in the include/ folder, and the related static libraries are provided in the lib/x86/vc12 folder.

• For Linux and Mac users, a Makefile is provided to compile the source code.
• • Requirements: OpenCV 2.4.x (it only works with this version).
• • Check out the latest project source code and compile it:

~/git clone https://github.com/andrewssobral/simple_vehicle_counting.git
~/cd simple_vehicle_counting
~/simple_vehicle_counting/cd build
~/simple_vehicle_counting/build/ cmake ..
~/simple_vehicle_counting/build/ make

• • Run demo:

~/simple_vehicle_counting/run_simple_vehicle_counting.sh

• Docker image is available at:
• • Ubuntu 16.04 + VNC + OpenCV 2.4.13 + Python 2.7 + Vehicle Detection, Tracking and Counting https://hub.docker.com/r/andrewssobral/vehicle_detection_tracking_counting/

#include <iostream>
#include <cv.h>
#include <highgui.h>

#include "package_bgs/PBAS/PixelBasedAdaptiveSegmenter.h"
#include "package_tracking/BlobTracking.h"
#include "package_analysis/VehicleCouting.h"

int main(int argc, char **argv)
{
  /* Open video file */
  CvCapture *capture = 0;
  capture = cvCaptureFromAVI("dataset/video.avi");
  if(!capture){
    std::cerr << "Cannot open video!" << std::endl;
    return 1;
  }
  
  /* Background Subtraction Algorithm */
  IBGS *bgs;
  bgs = new PixelBasedAdaptiveSegmenter;
  
  /* Blob Tracking Algorithm */
  cv::Mat img_blob;
  BlobTracking* blobTracking;
  blobTracking = new BlobTracking;

  /* Vehicle Counting Algorithm */
  VehicleCouting* vehicleCouting;
  vehicleCouting = new VehicleCouting;

  std::cout << "Press 'q' to quit..." << std::endl;
  int key = 0;
  IplImage *frame;
  while(key != 'q')
  {
    frame = cvQueryFrame(capture);
    if(!frame) break;

    cv::Mat img_input(frame);
    cv::imshow("Input", img_input);

    // bgs->process(...) internally process and show the foreground mask image
    cv::Mat img_mask;
    bgs->process(img_input, img_mask);
    
    if(!img_mask.empty())
    {
      // Perform blob tracking
      blobTracking->process(img_input, img_mask, img_blob);

      // Perform vehicle counting
      vehicleCouting->setInput(img_blob);
      vehicleCouting->setTracks(blobTracking->getTracks());
      vehicleCouting->process();
    }

    key = cvWaitKey(1);
  }

  delete vehicleCouting;
  delete blobTracking;
  delete bgs;

  cvDestroyAllWindows();
  cvReleaseCapture(&capture);
  
  return 0;
}

• Version 1.0.0: First version.