ViBe: A Universal Background Subtraction Algorithm for Video Sequences

TLDR

This approach differs from those based upon the classical belief that the oldest values should be replaced first. This paper presents a technique for motion detection that incorporates several innovative mechanisms. The method stores, for each pixel, a set of past values, compares it to the current pixel to decide background membership, adapts the model by randomly substituting values, propagates background to neighboring pixels, and is described in full detail with pseudo‑code and parameter values. Efficiency figures show that the method outperforms recent state‑of‑the‑art background subtraction techniques in both computation speed and detection rate, and even a simplified version with one comparison and one byte of memory per pixel outperforms mainstream techniques.

Abstract

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 upon 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 pseudo-code 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.