ViBe:一种高效的视频背景建模算法

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"很好的背景建模算法"这篇论文介绍了一种名为ViBe的创新背景建模技术，它发表在2011年IEEE Transactions on Image Processing第20卷第6期。该算法在视频序列背景检测领域具有显著优势，特别体现在处理噪声、光照变化和场景突变等复杂情况时的高效性和准确性。 ViBe算法的核心思想在于存储每个像素在过去特定位置或邻域内的值集合，而非仅仅依赖最旧的值来更新背景模型。这种机制使得算法能够适应实时变化的场景，不拘泥于传统的“先替换旧值”的策略。对于运动检测，算法会对比当前像素值与背景模型中的值，判断其是否属于背景，并动态选择合适的价值进行替换。这提高了算法对动态环境的敏感性。此外，当一个像素被识别为背景时，其值会被传播到相邻像素的背景模型中，进一步增强整体模型的鲁棒性。论文详尽地介绍了ViBe算法的实现细节，包括伪代码和使用的参数设置，使其具备了较高的可操作性和可扩展性。与现有的背景减除技术相比，如最近和已证实的最先进的方法，ViBe在效率指标上表现出明显的优势。这意味着它不仅能在保持准确性的前提下，提供更快的处理速度，对于实时视频监控和计算机视觉应用具有重要的实用价值。通过本文的研究，ViBe算法为背景建模领域的研究者和开发者提供了一个新的、值得深入探讨和优化的框架。"

1712 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 6, JUNE 2011

fading in the background, two additional mechanisms (one at

the pixel level, a second at the blob level) are added to the con-

sensus 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. D

ESCRIPTION OF A

UNIVERSAL

BACKGROUND

SUBTRACTION TECHNIQUE:V

IBE

Background subtraction techniques have to deal with at least

three considerations in order to be successful in real applica-

tions: 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 initial-

ization, 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

to that of a model with a given tolerance . They consider

that there is a good match if the absolute difference between

and is lower than . To be adaptive over time, is adjusted

with respect to the statistical variance of

. But the statistical

variance is estimated by a temporal average. Therefore, the ad-

justment speed is dependent upon 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 fram-

erate is determined by the available bandwidth.

We detail in the following a background subtraction tech-

nique, called visual background extractor (ViBe). For conve-

nience, 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 sta-

tistical 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 background subtrac-

tion 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 con-

sider 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 ubiq-

uitous. Furthermore, the evaluation of the pdf is a global process

and the shape of a pdf is sensitive to outliers. In addition, the es-

timation of the pdf raises the nonobvious question regarding the

number of samples to be considered; the problem of selecting a

representative number of samples is intrinsic to all the estima-

tion processes.

If we see the problem of background subtraction as a classi-

ﬁcation problem, we want to classify a new pixel value with re-

spect 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 cur-

rent 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 ig-

noring the extremities of the pdf, or to considering only the cen-

tral part of the underlying pdf by thresholding it. On the other

hand, if one trusts the values of the model, it is crucial to se-

lect 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 back-

ground models.

Formally, let us denote by

the value in a given Euclidean

color space taken by the pixel located at

in the image, and by

a background sample value with an index . Each background

pixel

is modeled by a collection of background sample

values

(1)

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

this is discussed later.

To classify a pixel value

according to its corresponding

model

, we compare it to the closest values within the

set of samples by deﬁning a sphere

of radius cen-

tered on

. The pixel value is then classiﬁed as back-

ground if the cardinality, denoted

, of the set intersection of this

sphere and the collection of model samples

is larger than

or equal to a given threshold

. More formally, we compare

(2)

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