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Foreground detection

전경 감지는 이미지 시퀀스의 변화를 감지하는 것을 목표로하는 컴퓨터 비전 및 이미지 처리 분야의 주요 작업 중 하나입니다.

Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. #Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.).

Many applications do not need to know everything about the evolution of movement in a video sequence, but only require the information of changes in the scene, because an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique.

Foreground detection separates foreground from background based on these changes taking place in the foreground. It is a set of techniques that typically analyze video sequences recorded in real time with a stationary camera.

Background subtraction

Background subtraction, also known as Foreground Detection, is a technique in the fields of image processing and computer vision wherein an image's foreground is extracted for further processing (object recognition etc.). Generally an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique. Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called “background image”, or “background model”. Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human poses estimation. However, background subtraction is generally based on a static background hypothesis which is often not applicable in real environments. With indoor scenes, reflections or animated images on screens lead to background changes. In a same way, due to wind, rain or illumination changes brought by weather, static backgrounds methods have difficulties with outdoor scenes.

Background Modeling

배경 추출을 잘 하려면 배경을 모델링을 하는 과정을 거쳐야 한다.

Documentation

A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos
http://www.academia.edu/5615720/A_comprehensive_review_of_background_subtraction_algorithms_evaluated_with_synthetic_and_real_videos
Github: A Background Subtraction Library
BGSLibrary: An OpenCV C++ Background Subtraction Library

Deep learning based

Deep Background Subtraction with Scene-Specific Convolutional Neural Networks
https://orbi.ulg.ac.be/bitstream/2268/195180/1/Braham2016Deep.pdf
Braham2016Deep.pdf

See also

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