Evaluating the Performance of Common Background Subtraction Techniques release_vc7p6eu25jgk5ostpfto6ucdd4

by Altahir Abdalla, Awab Samir, Hamza Abdulbagi, Zain Salah, Siddig Mohammed, Yasir Massad

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Abstract

recognizing moving objects from a video stream considered to be a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. This paper compares various background subtraction algorithms for detecting a single object. The work considers approaches varying from simple techniques such as static method and frame differencing to more sophisticated probabilistic modeling techniques such as adaptive median filtering and GMM. The evaluating process is based on visual observation of the output of the background subtraction techniques under assessments.
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