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Keywords :
Background; Background subtraction; Foreground; Video processing; Learning; Segmentation; Computer vision; Embedded systems; Real-time processing; Motion; Motion detection; Motion analysis; Pixel classification; Mixture of Gaussians; Benchmarking; Exponential filtering; Random sampling; Initialization; Background model; Fast algorithm; Algorithm; Comparision; Code; Pseudo-code; C code; C/C++; Smart camera; Kernel density estimation; Ghost; OpenCV; ViBe
Abstract :
[en] This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based on the classical belief that the oldest values should be replaced first.
Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudocode and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques. There is a dedicated web page for ViBe at http://www.telecom.ulg.ac.be/research/vibe/
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