【摘要】：前向運(yùn)動(dòng)的視頻拍攝作為一種移動(dòng)式的場(chǎng)景獲取方式,由于其視野寬闊,空間覆蓋面廣,已經(jīng)被廣泛應(yīng)用于移動(dòng)式的場(chǎng)景監(jiān)控和目標(biāo)檢測(cè)任務(wù)中。然而,隨著視頻資源的數(shù)據(jù)量不斷增加,許多算法由于計(jì)算復(fù)雜性逐漸無法滿足檢測(cè)任務(wù)的實(shí)時(shí)性要求,而且龐大的視頻數(shù)據(jù)量也為數(shù)據(jù)的存儲(chǔ)和檢索造成了困難。本文的研究基于攝像機(jī)前向運(yùn)動(dòng)拍攝的視頻數(shù)據(jù),從理論和應(yīng)用兩個(gè)方面展開論文的工作。首先提出了基于檢測(cè)區(qū)域幾何結(jié)構(gòu)的全景環(huán)帶采樣方法,并構(gòu)建快速的前向運(yùn)動(dòng)視頻的全景拼接算法,將海量的視頻數(shù)據(jù)進(jìn)行無損的信息抽取,得到了輕量級(jí)的全景圖格式,不僅降低了視頻數(shù)據(jù)的存儲(chǔ)和訪問開銷,而且將視頻轉(zhuǎn)化為一種更適合于人工檢視或計(jì)算機(jī)分析處理的形式。之后,以鐵路軌道狀態(tài)和護(hù)欄缺損檢測(cè)為應(yīng)用研究背景,提出了相應(yīng)的基于全景圖的自動(dòng)化視覺檢測(cè)算法。本論文的創(chuàng)新工作如下：1.提出了一種基于檢測(cè)區(qū)域幾何結(jié)構(gòu)的全景采樣模型。不同于以往的視頻拼接模型和圖像對(duì)齊方法,本文提出的基于檢測(cè)區(qū)域幾何結(jié)構(gòu)的全景采樣方法可以快速簡(jiǎn)潔地從前向運(yùn)動(dòng)視頻中生成全景圖。在相機(jī)的內(nèi)部參數(shù)已標(biāo)定的情況下,僅僅依靠攝像機(jī)的運(yùn)動(dòng)信息和空間場(chǎng)景的幾何結(jié)構(gòu)先驗(yàn),完成了拼接區(qū)域的構(gòu)造和對(duì)齊,沒有執(zhí)行耗時(shí)的圖像匹配和復(fù)雜的光流計(jì)算,實(shí)現(xiàn)了鐵路環(huán)境視頻的實(shí)時(shí)拼接,從而確保了基于全景圖的后續(xù)視覺檢測(cè)的實(shí)時(shí)性。2.提出了一種基于“雙縫投影”的單目立體全景成像方法。首先構(gòu)建了立體全景采樣的“雙縫投影”模型從每一幀圖像中抽取一對(duì)具有不同視角的拼接條帶,用來生成兩幅具有顯著視差的全景圖；然后分析了立體全景圖成像的原理來推導(dǎo)全景圖深度的計(jì)算公式；最后利用全景圖拼接方法生成了立體全景圖像對(duì),并基于局部立體匹配算法來估算“像位差”,從而獲得空間場(chǎng)景的深度信息。立體全景圖具有更廣闊的視野和更強(qiáng)的真實(shí)感,場(chǎng)景的深度信息也為人工目視檢測(cè)或計(jì)算機(jī)自動(dòng)識(shí)別提供了更加豐富的決策信息。3.提出了一種基于軌道全景圖(Rail track panorama)的鋼軌健康狀態(tài)的自動(dòng)感知方法。在物理空間中,由于軌道不良狀態(tài)而引起的列車晃動(dòng),勢(shì)必引起圖像空間中軌道全景圖(RTP)的鋼軌形態(tài)的變化。首先通過分析列車晃動(dòng)下的幾何成像模型,推導(dǎo)出“軌道不良狀態(tài)—列車晃動(dòng)—鋼軌圖像失真”之間的聯(lián)系。之后利用圖像閾值分割和形態(tài)學(xué)濾波的方法從軌道全景圖上提取鋼軌輪廓圖像,并分析鋼軌輪廓圖像的失真變化來反演出軌道的健康狀態(tài)。4.提出了一種基于護(hù)欄全景圖(Fence panorama)的護(hù)欄缺損的自動(dòng)化檢測(cè)方法。一旦獲得整個(gè)連續(xù)護(hù)欄的全景圖,那么就可以首先利用閾值分割的方法自動(dòng)提取護(hù)欄中豎直欄桿的位置,使其與背景圖像分離：其次根據(jù)分析相鄰欄桿之間的距離進(jìn)行護(hù)欄缺損判別。本文提出了一種“基于MVG三維直方圖的最大熵閾值分割方法”實(shí)現(xiàn)了欄桿位置的定位,并基于行程編碼的思想將護(hù)欄全景圖進(jìn)行編碼,壓縮的編碼格式包含了護(hù)欄的全部位置信息,大幅降低了存儲(chǔ)開銷,是一種有效的表示方法和存儲(chǔ)格式。同時(shí)也設(shè)計(jì)了相應(yīng)的解碼算法從編碼中恢復(fù)護(hù)欄的位置并實(shí)現(xiàn)了護(hù)欄的缺失檢測(cè)。
[Abstract]:As a moving scene acquisition mode, forward motion video capture has been widely used in mobile scene monitoring and target detection tasks due to its wide field of vision and wide space coverage. However, with the increasing amount of data in video resources, many algorithms are gradually unable to meet the detection tasks due to the complexity of computing. The real time requirement and the huge amount of video data have also caused difficulties in the storage and retrieval of data. This study is based on two aspects of the theory and application of video data taken from the forward motion of the camera. First, a panoramic band sampling method based on the detection of the geometric structure of the region is proposed, and the rapid development of the method is proposed. The panoramic splicing algorithm for forward motion video is used to extract massive video data from nondestructive information and obtain a lightweight panorama format. It not only reduces the storage and access overhead of video data, but also transforms video into a form that is more suitable for artificial viewing or computer analysis. As the application research background, the automatic visual detection algorithm based on panorama is proposed. The innovation work of this paper is as follows: 1. a panoramic sampling model based on the detection region geometry is proposed. The proposed method is based on the detection of the video mosaic model and the image alignment method. The panorama of the regional geometric structure can quickly and succinctly generate panoramic images from the previous motion video. In the case of the calibration of the camera's internal parameters, the construction and alignment of the stitching region is completed only by the motion information of the camera and the geometric structure of the space scene, and the time-consuming image matching and complex is not performed. The real-time mosaic of the railway environment video is realized by the miscellaneous optical flow calculation, which ensures the real-time.2. based on the follow-up vision detection based on the panorama. A single stereo panoramic imaging method based on the "double slit projection" is proposed. The splice strips with different perspectives are used to generate two panoramic images with significant parallax, and then analyze the principle of stereotactic imaging to derive the calculation formula for the depth of the panorama. Finally, the panoramic image pair is generated by the panoramic image stitching method, and the "image bit difference" is estimated based on the local erect matching algorithm. The depth information of the space scene is obtained. The stereoscopic panorama has a broader vision and a stronger sense of authenticity. The depth information of the scene provides a more abundant decision information for artificial visual inspection or computer automatic recognition..3. proposes an automatic perception side for the health of rail based on the Rail track panorama. Method. In the physical space, the train sloshing due to the bad state of the track will cause the change of the rail form of the track panorama (RTP) in the image space. First, by analyzing the geometric imaging model under the sloshing of the train, the connection between the bad state of the train and the distortion of the rail image in the train is deduced. Such as threshold segmentation and morphological filtering, the rail profile is extracted from the track panorama, and the distortion of the rail profile is analyzed to reverse the health state of the track. An automatic test method based on the guardrail defect based on the fence panorama (Fence panorama) is proposed. Once the panorama of the whole continuous guardrail is obtained. Then, the position of the vertical railing in the guardrail can be automatically extracted by the method of threshold segmentation, so that it can be separated from the background image. Secondly, according to the analysis of the distance between adjacent rails, a kind of "maximum entropy threshold segmentation method based on MVG 3D histogram" is proposed to implement the railing position. It encodes the panoramic view of the guardrail based on the idea of stroke coding. The compression coding format contains all the location information of the guardrail, greatly reduces the storage cost. It is an effective representation method and storage format. At the same time, the corresponding decoding algorithm is designed to restore the position of the guardrail from the code and realize the lack of the guardrail. Misdetection.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.41;R339.14

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