ViBe：视频序列通用背景减法算法

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"VIBE背景帧差法是一种视频序列的通用背景减除算法，由Olivier Barnich和Marc Van Droogenbroeck在2011年的IEEE Transactions on Image Processing期刊上发表。该方法旨在改进传统的运动检测技术，通过创新机制来更准确地识别背景和前景。与基于最古老值应首先被替换的传统信念不同，VIBE算法采用了一种不同的策略，即对每个像素存储过去在同一位置或邻近区域的值集合，并将当前像素值与此集合进行比较，以判断该像素是否属于背景。如果像素被认为是背景的一部分，其值会被传播到相邻像素的背景模型中。算法的伪代码和参数设置也在文章中详尽阐述，并与现有的背景减除技术进行了对比，显示了VIBE在效率上的优越性。" VIBE（ViBe: A Universal Background Subtraction Algorithm for Video Sequences）是针对视频序列的一种背景减除算法，其核心思想是动态更新背景模型。在传统帧差法中，通常假设最旧的像素值应该最先被新的像素值替换，以适应背景的变化。然而，VIBE打破了这个假设，它采用了更为灵活的方法来处理背景模型的更新。 VIBE算法的关键特点包括： 1. 像素值历史记录：对于每个像素，VIBE不仅存储当前像素的历史值，还包括邻近像素的值。这样可以更全面地捕捉背景的动态变化。 2. 模型更新策略：在比较当前像素值与存储的历史值后，VIBE随机选择要替换的背景模型值，而不是按照时间顺序强制替换最旧的值。这种随机性有助于适应非线性的背景变化，如光照突变或缓慢移动的物体。 3. 像素传播：当一个像素被认为是背景时，其值会传递给邻近像素的背景模型。这种局部传播机制有助于保持背景模型的一致性，减少误检的可能性。 4. 效率和性能：文章中，VIBE算法的效率和性能与一些最新的、经过验证的背景减除技术进行了比较，并显示出优势。这表明VIBE在处理复杂背景和快速运动物体时有更高的准确性和鲁棒性。通过这些创新机制，VIBE算法能够有效地处理多种背景环境下的视频序列，提高运动检测的准确性和可靠性，因此在实际应用中，如监控系统、自动驾驶和视频分析等领域具有广泛的应用潜力。

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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