基于視頻的車輛檢測系統(tǒng)

基于視頻的車輛檢測系統(tǒng),基于,視頻,車輛,檢測,系統(tǒng) 編號 無錫太湖學院 畢業(yè)設計（論文） 題目： 基于視頻的車輛檢測系統(tǒng) 信機 系 計算機科學與技術(shù) 專業(yè) 學 號： 　　　 0921157　　　　 學生姓名： 陳嘉斌 指導教師： 李朝鋒 （職稱：副教授 ） （職稱： ） 2013年5月25日 目 錄 一、畢業(yè)設計（論文）開題報告 二、畢業(yè)設計（論文）外文資料翻譯及原文 三、學生“畢業(yè)論文（論文）計劃、進度、檢查及落實表” 四、實習鑒定表 無錫太湖學院 畢業(yè)設計（論文） 開題報告 題目： 基于視頻的車輛檢測系統(tǒng) 信機 系 計算機科學與技術(shù) 專業(yè) 學 號： 0921157 學生姓名： 陳嘉斌 指導教師： 李朝鋒 （職稱：副教授 ） （職稱： ） 2011年11月20日 課題來源 導師指定 科學依據(jù)（包括課題的科學意義；國內(nèi)外研究概況、水平和發(fā)展趨勢；應用前景等） 　　　現(xiàn)如今，隨著我國國民經(jīng)濟的快速發(fā)展，居民的收入水平越來越高，汽車已經(jīng)成為了十分普遍的交通工具。對汽車的檢測與管理日趨重要。同時基于視頻以及以計算機視覺為基礎(chǔ)的對車輛進行識別檢測的理論越來越多。通過對圖像進行分析，能夠?qū)煌ㄐ畔⑦M行全方位的管理?？梢酝ㄟ^分析相關(guān)數(shù)據(jù)來評估和預測特定時間段的車輛情況。對圖像進行分析可以對相關(guān)交通狀態(tài)進行檢測，例如：車速檢測、流量檢測等；以及對一些異常狀況進行檢測，例如：超速檢測、車禍檢測，等一系列違章檢測。從而實現(xiàn)對交通運行的智能管理與控制。 一個完善的汽車檢測既幫助管理人員對交通狀況進行及時、準確的分析和處理最大限度地降低處理信息的勞動強度，使交通管理水平產(chǎn)生質(zhì)的飛躍，跟上信息時代的步伐。 研究內(nèi)容 通過對視頻逐幀選取圖像，然后對每一幀的圖像進行處理，最后再將處理后的沒幀圖像按原來的順序連接成視頻。本系統(tǒng)可以根據(jù)圖像中灰度的變化來確定汽車的存在與位置等。 擬采取的研究方法、技術(shù)路線、實驗方案及可行性分析 本系統(tǒng)運用MATLAB圖像分割技術(shù)來檢測運動中的汽車。由于視頻文件是圖像一幀一幀按順序組成的，所以對于視頻文件的處理同樣可以用對圖像處理的方法。逐幀截取圖像對其進行處理，然后再將他們重新按順序連接起來。在處理圖像時，將會使用很多視頻處理函數(shù)，例如讀取文件的mmreader函數(shù)，使視頻可視化的implay函數(shù)，還將涉及到數(shù)學形態(tài)學的函數(shù)，例如imextendedmax，imopen，bwareaopen等。 本系統(tǒng)首先會使用mmreader函數(shù)讀取視頻，此函數(shù)可支持多種視頻格式，如MPG.MPEG,WMV,AVI,ASF,ASX。然后使用implay函數(shù)播放。選取一些具有代表性的圖像幀，將其傳化成灰度圖像。使用imextendedmax函數(shù)來取出深色大目標，再使用imopen函數(shù)來去除一些小目標。最后可以進行其他類型的檢測。 研究計劃及預期成果 2012年12月4日以前：填寫《畢業(yè)設計開題報告》，并按開題報告條款進入畢業(yè)設計階段 2012年12月~2013年1月：外文資料翻譯，系統(tǒng)設計 2013年2月：系統(tǒng)設計、編碼 2013年3月~2013年4月：測試、驗收，撰寫畢業(yè)論文 2013年5月：上交論文、系統(tǒng)代碼、根據(jù)導師意見修改畢業(yè)論文并完善論文 2013年6月2日—4日，進行畢業(yè)答辯 預期成果：每步安排都可以按時完美完成 特色或創(chuàng)新之處 本課題是使用MATLAB圖像分割技術(shù)。讀取一幀圖像并檢測圖像中的汽車，然后使用循環(huán)逐幀對圖像進行檢測。 已具備的條件和尚需解決的問題 已具備的條件： 1、已知系統(tǒng)的初步需求。 2、硬件方面有一臺計算機。 指導教師意見 指導教師簽名： 年 月 日 教研室（學科組、研究所）意見 教研室主任簽名： 年 月 日 系意見 主管領(lǐng)導簽名： 年 月 日 英文原文 Digital Image Processing and Edge Detection 1. Digital Image Processing Interest in digital image processing methods stems from two principal applicant- ion areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for au- tenuous machine perception. An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, peels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trump, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- son, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be- teen image processing and computer vision. There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high-level processes. Low-level processes involve primitive opera- tons such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher-level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision. Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.” As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value. The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied. Images based on radiation from the EM spectrum are the most familiar, esp. - especially images in the X-ray and visual bands of the spectrum. Electromagnet- ice waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of mass less particles, each traveling in a wavelike pattern and moving at the speed of light. Each mass less particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other. Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling. Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result. Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image. Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions. Compression, as the name implies, deals with techniques for reducing the storage required saving an image, or the bandwidth required transmitting it. Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard. Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes. Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the bound- ray of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for trans- forming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another. Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects. So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as slim- plea as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in con- lection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as op- posed to single-headed arrows linking the processing modules. 2. Edge detection Edge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Although point and line detection certainly are important in any discussion on segmentation, edge detection is by far the most common approach for detecting meaningful disco unties in gray level. Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects: 1.focal blur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local secularities or antireflections in the vicinity of object edges. A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line. To illustrate why edge detection is not a trivial task, let us consider the problem of detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels. 5 7 6 4 152 148 149 If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighboring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges. Hence, to firmly state a specific threshold on how large the intensity change between two neighboring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled. There are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Appalachian or the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction). The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions. Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking out irrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges. If the edge shareholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction. A commonly used approach to handle the problem of appropriate thresholds for shareholding is by using shareholding with hysteretic. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge w