# simple_vehicle_counting

**Repository Path**: tangran1/simple_vehicle_counting

## Basic Information

- **Project Name**: simple_vehicle_counting
- **Description**: Vehicle Detection, Tracking and Counting
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-07
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 :)

<p align="center"><img src="https://sites.google.com/site/andrewssobral/vehicle_counting_screen.png" /></p>


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 <iostream>
#include <opencv2/opencv.hpp>

#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.