ViBe：一种视频序列的通用背景减除算法

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"Visual Background subtraction for video sequence - 使用VIBE算法进行视频序列的背景建模" 在计算机视觉领域，背景减除（Background Subtraction）是一种常用的目标检测方法，它旨在识别视频序列中的运动对象。该技术通过区分静态背景与动态前景来实现目标检测。"Visual Background subtraction for video sequence"这一主题探讨了一种名为VIBE（Universal Background Subtraction Algorithm for Video Sequences）的通用背景减除算法，其在处理效果和实时性上表现出色，与混合高斯模型（GMM）和基于核函数的方法相比更为简洁。 VIBE算法的核心在于它的创新机制。首先，对于每个像素，算法会存储该位置或邻近位置在过去捕获的一系列历史值。然后，它将这些历史值集合与当前像素值进行比较，以此判断像素是否属于背景。与传统的背景建模方法不同，VIBE不是优先替换最旧的值，而是随机选择背景模型中的值进行替换，这使得模型能够更好地适应环境变化。此外，当算法确定某个像素属于背景时，它的值会被传播到相邻像素的背景模型中，增强了模型的连贯性和稳定性。这种方法有助于减少由于光照变化、阴影以及摄像机抖动等因素导致的误检测。论文提供了VIBE算法的详细描述，包括伪代码和实际使用的参数值，以便其他研究者复现和评估。实验结果表明，VIBE在效率和性能上超越了近期的先进方法，进一步证明了其在背景减除领域的有效性。 "Visual Background subtraction for video sequence"关注的是VIBE算法，这是一种高效且具有较高实时性的背景建模技术，其创新的像素值存储和更新策略，以及对相邻像素背景模型的影响，使其在视频分析和目标检测的应用中具有显著优势。

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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