Vibe算法：视频序列的通用背景分割

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本文档主要介绍了一种名为"Vibe"的视频分割算法，它在IEEE版权许可下由Olivier Barnich和Marc Van Droogenbroeck提出，两位作者均为IEEE成员。Vibe算法作为一种创新的背景检测方法，针对视频序列设计，旨在提高运动检测的准确性和效率。该算法的核心思想在于它为每个像素存储一个过去位置或其周围像素值的集合。这样做的目的是捕捉到像素值随时间的变化模式，从而区别前景和背景。传统的背景检测方法往往基于一个假设，即较旧的像素值应该优先替换，而Vibe算法则采用了不同的策略。它通过随机选择背景模型中的值来更新当前像素，这种方法使得算法更加灵活，能够适应更复杂的场景变化。 Vibe算法的独特之处在于其动态更新机制：当一个像素被识别为背景时，它的值不仅会被应用于自身的背景模型，还会进一步传播到相邻像素的背景模型中。这种传播机制有助于保持整体背景模型的连续性和一致性，提高了整个视频序列的分割效果。相比于静态或规则更新策略，Vibe算法能更好地处理动态背景和快速运动物体，减少了误报和漏报的可能性。然而，值得注意的是，这篇文章尚未完成编辑，并可能在最终发表前进行修改。因此，读者在实际应用时应参考最新的版本，以获取最准确的技术细节。Vibe视频分割算法是一项具有创新性的技术，对于实时视频监控、行为分析等领域具有潜在的巨大价值。

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

further improvement for caches with more than 60 samples.

Consequently, the training period for their algorithm must

comprise at least 20 frames. Finally, to cope with lighting

changes and objects appearing or fading in the background,

two additional mechanisms (one at the pixel level, a second at

the blob level) are added to the consensus algorithm to handle

entire objects.

The method proposed in this paper operates differently in

handling new or fading objects in the background, without

the need to take account of them explicitly. In addition to

being faster, our method exhibits an interesting asymmetry

in that a ghost (a region of the background discovered once

a static object starts moving) is added to the background

model more quickly than an object that stops moving. Another

major contribution of this paper resides in the proposed update

policy. The underlying idea is to gather samples from the past

and to update the sample values by ignoring when they were

added to the models. This policy ensures a smooth exponential

decaying lifespan for the sample values of the pixel models and

allows our technique to deal with concomitant events evolving

at various speeds with a unique model of a reasonable size for

each pixel.

III. DESCRIPTION OF A UNIVERSAL BACKGROUND

SUBTRACTION TECHNIQUE: VIBE

Background subtraction techniques have to deal with at

least three considerations in order to be successful in real

applications: (1) what is the model and how does it behave?,

(2) how is the model initialized?, and (3) how is the model

updated over time? Answers to these questions are given in

the three subsections of this section. Most papers describe the

intrinsic model and the updating mechanism. Only a minority

of papers discuss initialization, which is critical when a fast

response is expected, as in the case inside a digital camera. In

addition, there is often a lack of coherence between the model

and the update mechanism. For example, some techniques

compare the current value of a pixel p to that of a model

b with a given tolerance T . They consider that there is a good

match if the absolute difference between p and b is lower

than T . To be adaptive over time, T is adjusted with respect

to the statistical variance of p. But the statistical variance is

estimated by a temporal average. Therefore, the adjustment

speed is dependent on the acquisition framerate and on the

number of background pixels. This is inappropriate in some

cases, as in the case of remote IP cameras whose framerate is

determined by the available bandwidth.

We detail below a background subtraction technique, called

“ViBe” (for “VIsual Background Extractor”). For convenience,

we present a complete version of our algorithm in a C-like

code in Appendix A.

A. Pixel model and classiﬁcation process

To some extent, there is no way around the determination,

for a given color space, of a probability density function (pdf)

for every background pixel or at least the determination of

statistical parameters, such as the mean or the variance. Note

that with a gaussian model, there is no distinction to be made

as the knowledge of the mean and variance is sufﬁcient to

determine the pdf. While the classical approaches to back-

ground subtraction and most mainstream techniques rely on

pdfs or statistical parameters, the question of their statistical

signiﬁcance is rarely discussed, if not simply ignored. In

fact, there is no imperative to compute the pdf as long as

the goal of reaching a relevant background segmentation is

achieved. An alternative is to consider that one should enhance

statistical signiﬁcance over time, and one way to proceed is to

build a model with real observed pixel values. The underlying

assumption is that this makes more sense from a stochastic

point of view, as already observed values should have a higher

probability of being observed again than would values not yet

encountered.

Like the authors of [65], we do not opt for a particular

form for the pdf, as deviations from the assumed pdf model

are ubiquitous. Furthermore, the evaluation of the pdf is a

global process and the shape of a pdf is sensitive to outliers.

In addition, the estimation of the pdf raises the non-obvious

question regarding the number of samples to be considered;

the problem of selecting a representative number of samples

is intrinsic to all the estimation processes.

If we see the problem of background subtraction as a

classiﬁcation problem, we want to classify a new pixel value

with respect to its immediate neighborhood in the chosen

color space, so as to avoid the effect of any outliers. This

motivates us to model each background pixel with a set of

samples instead of with an explicit pixel model. Consequently

no estimation of the pdf of the background pixel is performed,

and so the current value of the pixel is compared to its

closest samples within the collection of samples. This is an

important difference in comparison with existing algorithms,

in particular with those of consensus-based techniques. A new

value is compared to background samples and should be close

to some of the sample values instead of the majority of all

values. The underlying idea is that it is more reliable to

estimate the statistical distribution of a background pixel with

a small number of close values than with a large number of

samples. This is somewhat similar to ignoring the extremities

of the pdf, or to considering only the central part of the

underlying pdf by thresholding it. On the other hand, if one

trusts the values of the model, it is crucial to select background

pixel samples carefully. The classiﬁcation of pixels in the

background therefore needs to be conservative, in the sense

that only background pixels should populate the background

models.

Formally, let us denote by v(x) the value in a given

Euclidean color space taken by the pixel located at x in the

image, and by v

a background sample value with an index

i. Each background pixel x is modeled by a collection of N

background sample values

M(x) = { v

, v

, . . . , v

} (1)

taken in previous frames. For now, we ignore the notion of

time; this is discussed later.

To classify a pixel value v(x) according to its corresponding

model M(x), we compare it to the closest values within the

set of samples by deﬁning a sphere S

(v(x)) of radius R

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