城市交通分析的计算机视觉技术综述

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"这篇论文是关于计算机视觉技术在城市交通分析中的应用的综合评论，重点关注了当前的技术状态、挑战和未来研究方向。随着硬件成本的降低和监控摄像头部署的增加，视频分析在智能交通系统（ITS）中的角色日益重要。文章讨论了如何利用这些摄像头进行交通拥堵、交通违规和车辆交互等的监测。尽管高速公路车辆检测和分类已有经典视觉监控技术，如背景估计和运动跟踪，但城市环境因其高密度交通、大量遮挡和多样化的道路使用者而更具挑战性。因此，来自对象分类和3D建模的方法激发了更先进的技术来应对这些挑战。然而，缺乏统一的数据集或基准测试使得算法之间的直接比较变得困难，同时，对于恶劣天气条件下的评估也亟待加强。未来的努力应该集中在开发适用于所有道路使用者的鲁棒联合检测器和分类器，并在现实条件下进行评估。" 这篇文章详细阐述了计算机视觉在城市交通分析中的应用，主要包括以下几个知识点： 1. 自动视频分析：城市监控摄像头的自动分析技术基于计算机视觉，是近年来快速发展的一个领域。这种技术可以实时处理和理解交通场景，从而实现智能化的交通管理。 2. 智能交通系统（ITS）：计算机视觉技术对ITS的发展至关重要，因为它可以提供实时的交通信息，帮助预测和解决交通问题，如拥堵、事故预防和交通规则执行。 3. 背景估计和运动跟踪：这些是传统的视觉监控技术，常用于高速公路的车辆检测和分类。通过估计背景并追踪移动物体，可以识别出道路上的车辆。 4. 城市环境的挑战：与高速公路相比，城市环境的交通密度更高，相机角度较低，导致遮挡情况严重，且道路使用者多样化，这些因素都对计算机视觉技术提出了更高的要求。 5. 对象分类和3D建模：这些先进技术用于应对城市环境中的挑战，通过识别不同类型的物体（如行人、自行车、汽车等）和构建3D模型，提高分析的准确性和鲁棒性。 6. 缺乏统一数据集和基准：目前，没有广泛接受的数据集或标准测试来评估算法性能，这使得技术对比和进步受到限制。 7. 复杂环境下的评估：尽管实时分析是关键，但在恶劣天气（如雨、雾、黑暗）条件下的系统性能评估相对较少，这是未来研究的重要方向。 8. 未来研究方向：未来的研究应集中在开发能够适应各种道路用户和复杂环境的鲁棒检测和分类算法，并在实际环境中进行验证，以提高系统的实用性和可靠性。这篇论文提供了计算机视觉技术在城市交通分析领域的全面概述，强调了现有挑战和未来研究的重点，对于该领域的研究者和技术开发者具有重要参考价值。

BUCH et al.: REVIEW OF COMPUTER VISION TECHNIQUES FOR ANALYSIS OF URBAN TRAFFIC 923

Fig. 2. Block diagram for a top–down surveillance system. The grouping of

pixels in the foreground mask into silhouettes that represent objects is done

early with a simple algorithm without knowledge of object classes.

Fig. 3. Block diagram for a bottom–up surveillance system. Local image

patches are ﬁrst extracted from the input image and classiﬁed as a speciﬁc

part of a trained object class. The identiﬁed parts are combined into objects

based on the class through a grouping or voting process. Advanced tracking

concepts [83] allow this grouping to be performed in the spatial–temporal

domain, which directly produces an object trajectory rather than frame-per-

frame object detections.

the processing pipeline of a typical video analytics application:

foreground estimation (see Section III-A), classiﬁcation (see

Section III-B), and tracking (see Section III-D). See Fig. 2

for a block diagram. A statistical model typically estimates

foreground pixels, which are then grouped with a basic model

(e.g., connected regions) and propagated through the system

until the classiﬁcation stage; for example, see [14], [21], [29],

[45], [51], [56], and [98]. Classiﬁcation then uses prior infor-

mation (previously learned or preprogrammed) about the object

classes to assign a class label. For the remainder of this paper,

we will refer to this class of algorithms as “top–down” or

“object based,” because pixels are grouped into objects early

during the processing.

In contrast, we deﬁne a “bottom–up” approach as an ap-

proach that ﬁrst detects and classiﬁes parts of an object (see

Fig. 3). This initial classiﬁcation of the parts uses learned prior

information (e.g., training) about the ﬁnal object classes (e.g.,

an image area is classiﬁed to be a car wheel or a pedestrian

head based on previously learned appearances of wheels and

heads). The combination of these parts into valid objects and

trajectories is the ﬁnal step of the algorithm; for example, see

[83], [85], and [104]. This type of approach is typically used in

generic object recognition.

In the next section, we will ﬁrst describe the top–down

approach in more detail, including foreground segmentation

and top–down vehicle classiﬁcation. This approach is followed

by relevant bottom–up classiﬁcation approaches for trafﬁc

surveillance. The last section considers tracking, which can

equally be applied after both classiﬁcation methods.

A. Foreground Segmentation

Foreground estimation and segmentation is the ﬁrst stage of

several visual surveillance systems. The f oreground regions are

marked (e.g., mask image) for processing in the subsequent

steps. The foreground is deﬁned as every object, which is

not a ﬁxed furniture of a scene, where ﬁxed could normally

mean months or years. This deﬁnition conforms to human

understanding, but it is difﬁcult to algorithmically implement.

There are two main different approaches to estimate the fore-

ground, which both use strong assumptions to comply with the

aforementioned deﬁnition. First, a background model of some

kind can be used to accumulate information about the s cene

background of a video sequence. The model is then compared

to the current frame to identify differences (or “motion”),

provided that the camera is stationary. This concept lends itself

well for computer implementation but leads to problems with

slow-moving trafﬁc. Any car should be considered foreground,

but stationary objects are missed due to the lack of motion. The

next ﬁve sections discus different solutions for using motion as

the main cue for foreground segmentation.

A different approach performs segmentation based on whole

object appearances and will be discussed in Section VI. This

approach can be used for moving and for stationary cameras but

requires prior information f or foreground object appearances.

This way, the review moves from the simple frame differ-

ence method in the next section to learning based methods in

Section VI.

1) Frame Differencing: Possibly, the simplest method for

foreground segmentation is frame differencing. A pixel-by-

pixel difference map is computed between two consecutive

frames. This difference is thresholded and used as the fore-

ground mask. This algorithm is very fast; however, it can-

not cope with noise, abrupt illumination changes, or periodic

movements in the background such as trees. In [110], frame

differencing is used to detect street-parking vehicles. Special

care is taken in the algorithm to suppress the inﬂuence of noise.

Motorcycles are detected in [102] based on frame differencing.

However, using more information than only the last frame for

subtraction is preferable. This approach leads to the background

subtraction techniques described in the next sections.

2) Background Subtraction: This group of background

models estimates a background image (i.e., ﬁxed scene), which

is subtracted from the current video frame. A threshold is

applied to the resulting difference image to give the foreground

mask. The threshold can be constant or dynamic, as used in

[51]. The following methods differ in the way the background

picture is obtained, resulting in different levels of image quality

for different levels of computational complexity.

a) Averaging: In the background averaging method, all

video frames are summed up. The learning rate speciﬁes the

weight between a new frame and the background. This al-

gorithm has little computational cost; however, it is likely to

produce tails behind moving objects due to the contamination

of the background with the appearance of the moving objects.

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