How to Use Background Subtraction Methods

Background subtraction (BS) is a common and widely used technique for generating a foreground mask (namely, a binary image containing the pixels belonging to moving objects in the scene) by using static cameras.
As the name suggests, BS calculates the foreground mask performing a subtraction between the current frame and a background model, containing the static part of the scene or, more in general, everything that can be considered as background given the characteristics of the observed scene.
Background modeling consists of two main steps:

-# Background Initialization; -# Background Update.

In the first step, an initial model of the background is computed, while in the second step that model is updated in order to adapt to possible changes in the scene.
In this tutorial we will learn how to perform BS by using OpenCV. As input, we will use data coming from the publicly available data set Background Models Challenge (BMC) .

Goals

In this tutorial you will learn how to:

-# Read data from videos by using @ref cv::VideoCapture or image sequences by using @ref cv::imread ; -# Create and update the background model by using @ref cv::BackgroundSubtractor class; -# Get and show the foreground mask by using @ref cv::imshow ; -# Save the output by using @ref cv::imwrite to quantitatively evaluate the results.

Code

In the following you can find the source code. We will let the user chose to process either a video file or a sequence of images.

Two different methods are used to generate two foreground masks: -# cv::bgsegm::BackgroundSubtractorMOG -# @ref cv::BackgroundSubtractorMOG2

The results as well as the input data are shown on the screen. The source file can be downloaded here .

@includelineno samples/cpp/tutorial_code/video/bg_sub.cpp

Explanation

We discuss the main parts of the above code:

-# First, three Mat objects are allocated to store the current frame and two foreground masks, obtained by using two different BS algorithms. @code{.cpp} Mat frame; //current frame Mat fgMaskMOG; //fg mask generated by MOG method Mat fgMaskMOG2; //fg mask fg mask generated by MOG2 method @endcode -# Two @ref cv::BackgroundSubtractor objects will be used to generate the foreground masks. In this example, default parameters are used, but it is also possible to declare specific parameters in the create function. @code{.cpp} Ptr pMOG; //MOG Background subtractor Ptr pMOG2; //MOG2 Background subtractor ... //create Background Subtractor objects pMOG = createBackgroundSubtractorMOG(); //MOG approach pMOG2 = createBackgroundSubtractorMOG2(); //MOG2 approach @endcode -# The command line arguments are analysed. The user can chose between two options: - video files (by choosing the option -vid); - image sequences (by choosing the option -img). @code{.cpp} if(strcmp(argv[1], "-vid") == 0) { //input data coming from a video processVideo(argv[2]); } else if(strcmp(argv[1], "-img") == 0) { //input data coming from a sequence of images processImages(argv[2]); } @endcode -# Suppose you want to process a video file. The video is read until the end is reached or the user presses the button 'q' or the button 'ESC'. @code{.cpp} while( (char)keyboard != 'q' && (char)keyboard != 27 ){ //read the current frame if(!capture.read(frame)) { cerr << "Unable to read next frame." << endl; cerr << "Exiting..." << endl; exit(EXIT_FAILURE); } @endcode -# Every frame is used both for calculating the foreground mask and for updating the background. If you want to change the learning rate used for updating the background model, it is possible to set a specific learning rate by passing a third parameter to the 'apply' method. @code{.cpp} //update the background model pMOG->apply(frame, fgMaskMOG); pMOG2->apply(frame, fgMaskMOG2); @endcode -# The current frame number can be extracted from the @ref cv::VideoCapture object and stamped in the top left corner of the current frame. A white rectangle is used to highlight the black colored frame number. @code{.cpp} //get the frame number and write it on the current frame stringstream ss; rectangle(frame, cv::Point(10, 2), cv::Point(100,20), cv::Scalar(255,255,255), -1); ss << capture.get(CAP_PROP_POS_FRAMES); string frameNumberString = ss.str(); putText(frame, frameNumberString.c_str(), cv::Point(15, 15), FONT_HERSHEY_SIMPLEX, 0.5 , cv::Scalar(0,0,0)); @endcode -# We are ready to show the current input frame and the results. @code{.cpp} //show the current frame and the fg masks imshow("Frame", frame); imshow("FG Mask MOG", fgMaskMOG); imshow("FG Mask MOG 2", fgMaskMOG2); @endcode -# The same operations listed above can be performed using a sequence of images as input. The processImage function is called and, instead of using a @ref cv::VideoCapture object, the images are read by using @ref cv::imread , after individuating the correct path for the next frame to read. @code{.cpp} //read the first file of the sequence frame = imread(fistFrameFilename); if(!frame.data){ //error in opening the first image cerr << "Unable to open first image frame: " << fistFrameFilename << endl; exit(EXIT_FAILURE); } ... //search for the next image in the sequence ostringstream oss; oss << (frameNumber + 1); string nextFrameNumberString = oss.str(); string nextFrameFilename = prefix + nextFrameNumberString + suffix; //read the next frame frame = imread(nextFrameFilename); if(!frame.data){ //error in opening the next image in the sequence cerr << "Unable to open image frame: " << nextFrameFilename << endl; exit(EXIT_FAILURE); } //update the path of the current frame fn.assign(nextFrameFilename); @endcode Note that this example works only on image sequences in which the filename format is <n>.png, where n is the frame number (e.g., 7.png).

Results

Given the following input parameters: @code{.cpp} -vid Video_001.avi @endcode The output of the program will look as the following:
The video file Video_001.avi is part of the Background Models Challenge (BMC) data set and it can be downloaded from the following link Video_001 (about 32 MB).
If you want to process a sequence of images, then the '-img' option has to be chosen: @code{.cpp} -img 111_png/input/1.png @endcode The output of the program will look as the following:
The sequence of images used in this example is part of the Background Models Challenge (BMC) dataset and it can be downloaded from the following link sequence 111 (about 708 MB). Please, note that this example works only on sequences in which the filename format is <n>.png, where n is the frame number (e.g., 7.png).

Evaluation

To quantitatively evaluate the results obtained, we need to:

Save the output images;
Have the ground truth images for the chosen sequence.

In order to save the output images, we can use @ref cv::imwrite . Adding the following code allows for saving the foreground masks. @code{.cpp} string imageToSave = "output_MOG_" + frameNumberString + ".png"; bool saved = imwrite(imageToSave, fgMaskMOG); if(!saved) { cerr << "Unable to save " << imageToSave << endl; } @endcode Once we have collected the result images, we can compare them with the ground truth data. There exist several publicly available sequences for background subtraction that come with ground truth data. If you decide to use the Background Models Challenge (BMC), then the result images can be used as input for the BMC Wizard. The wizard can compute different measures about the accuracy of the results.

References

Background Models Challenge (BMC) website
A Benchmark Dataset for Foreground/Background Extraction @cite vacavant2013benchmark

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