VIBE技术：一种通用视频背景 subtraction 算法详解

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"这篇资源是关于VIBE(ViBe: A universal background subtraction algorithm for video sequences)技术的详细分享，由Olivier Barnich和Marc Van Droogenbroeck提出。该算法是一种创新的运动检测方法，用于视频背景 subtraction。文章在IEEE Transactions on Image Processing期刊上发表，提供伪代码和参数值，并与其他背景 subtraction 技术进行了对比分析。" VIBE背景 subtraction 算法是一种广泛应用于视频处理的技术，旨在识别和分离视频帧中的前景对象与背景。此算法的核心理念在于其独特的像素模型更新策略，它对于每个像素点，存储过去在同一位置或邻近区域的像素值集合。当比较当前像素值与这个历史值集合时，VIBE算法不是简单地基于最早的时间戳来替换背景模型中的旧值，而是随机选择哪些值进行替换。这种设计使得算法更具适应性，能够更好地应对光照变化、阴影等复杂环境因素。 VIBE算法的工作流程包括以下几个关键步骤： 1. **像素值存储与比较**：对每个像素，算法记录并维护一个历史值列表。在每帧新数据到来时，将当前像素值与这个历史集合进行比较。 2. **背景判定**：如果当前像素值与历史值集合中的大部分值接近，那么该像素被认为是背景的一部分。 3. **模型更新**：当确定一个像素属于背景时，算法随机选择历史集合中的某些值进行替换，以反映背景的变化。这种随机性有助于避免过早或过晚移除实际的背景变化。 4. **邻域传播**：为了提高背景模型的连贯性，背景像素值会被传播到邻近像素的背景模型中，增强了背景区域的一致性。在论文中，作者详细描述了算法的伪代码，提供了具体的参数设置，这使得其他研究者和开发者可以复现和评估算法的效果。此外，通过与其他背景 subtraction 方法的对比，VIBE算法的性能和优势得到了验证，可能包括更快的收敛速度、更好的抗噪声能力以及在复杂场景下的高准确率。 VIBE算法是对传统背景 subtraction 技术的一种革新，其随机更新策略和邻域传播机制使其在处理动态背景和光照变化的视频序列中表现出色。这篇分享不仅提供了算法原理，还包含了实现细节，对于理解背景 subtraction 技术和开发相关应用具有重要价值。

O. BARNICH and M. VAN DROOGENBROECK. ViBe : A universal background subtraction algorithm for video sequences. IEEE Transactions on

Image Processing, 20(6) :1709-1724, June 2011. doi :10.1109/TIP.2010.2101613

example, in the case of newly freed parking spots in urban

outdoor scenes).

Instead of choosing a particular form of background density

model, the authors of [64], [65] use the notion of “consensus”.

They keep a cache of a given number of last observed

background values for each pixel and classify a new value

as background if it matches most of the values stored in

the pixel’s model. One might expect that such an approach

would avoid the issues related to deviations from an arbitrarily

assumed density model, but since values of pixel models are

replaced according to a ﬁrst-in ﬁrst-out update policy, they are

also prone to the problems discussed previously, for example,

the problem of slow and fast motions in the background,

unless a large number of pixel samples are stored. The authors

state that a cache of 20 samples is the minimum required for

the method to be useful, but they also noticed no signiﬁcant

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

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