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https://github.com/andrewssobral/simple_vehicle_counting

Vehicle Detection, Tracking and Counting
https://github.com/andrewssobral/simple_vehicle_counting

background-subtraction opencv vehicle-counting vehicle-detection vehicle-tracking

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Vehicle Detection, Tracking and Counting

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README 

        
Vehicle Detection, Tracking and Counting

========================================



Last page update: **12/04/2017** (Added Python API & OpenCV 3.x support)



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](https://github.com/andrewssobral/bgslibrary#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](https://code.google.com/p/cvblob/) or [OpenCVBlobsLib](http://opencvblobslib.github.io/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 :)






Citation

--------

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"

}

```



For Windows users

-----------------

* There is no Visual Studio 2013 template project anymore. Please, use CMAKE instead.



#### Compiling with OpenCV 3.x and Visual Studio 2015 from CMAKE



**Dependencies:**

* OpenCV 3.x (tested with OpenCV 3.2.0)

* GIT (tested with git version 2.7.2.windows.1).

* CMAKE for Windows (tested with cmake version 3.1.1).

* Microsoft Visual Studio (tested with VS2015).



*Note: the procedure is similar for OpenCV 2.4.x and Visual Studio 2013.*



Please follow the instructions below:



1) Go to Windows console.



2) Clone git repository:

```

git clone --recursive https://github.com/andrewssobral/simple_vehicle_counting.git

```



3) Go to **simple_vehicle_counting/build** folder.



4) Set your OpenCV PATH:

```

set OpenCV_DIR=C:\OpenCV3.2.0\build

```



5) Launch CMAKE:

```

cmake -DOpenCV_DIR=%OpenCV_DIR% -G "Visual Studio 14 Win64" ..

```



6) Include OpenCV binaries in the system path:

```

set PATH=%PATH%;%OpenCV_DIR%\x64\vc14\bin

```



7) Open the **bgs.sln** file in your Visual Studio and switch to **'RELEASE'** mode



8) Click on **'ALL_BUILD'** project and build!



9) If everything goes well, copy **simple_vehicle_counting.exe** to **simple_vehicle_counting/** and run!



For Linux users

-----------------

* For Linux and Mac users, a CMakefile is provided to compile the source code.



* * Check out the latest project source code and compile it:

```

~/git clone --recursive 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

----------------------------------------

* 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/



Example code

------------

```C++

#include 

#include 



#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 = cv::cvarrToMat(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;

}

```



Python API

----------------------------------------

A [python demo](python/demo.py) shows how to call the Python API.

It is similar as the [C++ demo](Demo.cpp).



To use the Python API, you should copy ["python" directory](python) to overwrite the generated one.



```

~/simple_vehicle_counting/cd build

~/simple_vehicle_counting/build/cmake ..

~/simple_vehicle_counting/build/make -j 8

~/simple_vehicle_counting/build/cp -r ../python/* python/

~/simple_vehicle_counting/build/../run_python_demo.sh

```



If you have previously built the project at the project root,

make sure there are no previously generated libraries in the ["python" directory](python) by ```make clean```.



Release Notes:

--------------

* 12/04/2017: Added OpenCV 3.x support. Removed vs2013 template project (use CMAKE instead).



* 07/04/2017: Added Python API, thanks to [@kyu-sz](https://github.com/kyu-sz).



* Version 1.0.0: First version.