本文選題：視頻質(zhì)量評(píng)價(jià) + 車輛輪廓清晰化��； 參考：《昆明理工大學(xué)》2015年碩士論文

【摘要】：智能交通系統(tǒng)(Intelligent Transportation System)是未來(lái)交通系統(tǒng)的主流,先進(jìn)的交通控制和計(jì)算機(jī)科學(xué)有效的結(jié)合,將高效服務(wù)于人類,極大提高監(jiān)管以及違章肇事處理效率。目前,監(jiān)管部門對(duì)車輛的監(jiān)控執(zhí)法,主要通過車牌自動(dòng)識(shí)別系統(tǒng)識(shí)別車輛牌照,以此作為違法證據(jù)對(duì)其進(jìn)行責(zé)任的追究。而對(duì)于牌照故意遮擋、牌照有牌不掛以及使用套牌等違章行為,現(xiàn)有車牌自動(dòng)識(shí)別系統(tǒng)無(wú)法識(shí)別,需要通過人工辨別進(jìn)行處罰,耗時(shí)費(fèi)力。解決這一問題成為智能交通中亟待解決的熱點(diǎn)和難點(diǎn)。本文的研究?jī)?nèi)容是分布式視頻車輛同一性時(shí)空關(guān)聯(lián)檢索中的基礎(chǔ)研究工作,為視頻車輛同一性時(shí)空關(guān)聯(lián)檢索提供了重要的基礎(chǔ)技術(shù)支持。本文的主要研究點(diǎn)有以下幾個(gè)方面：1、視頻樣本建立及質(zhì)量分析評(píng)價(jià)。針對(duì)高速公路卡口角度不同、道路環(huán)境不同、天氣和光線的時(shí)常變化等因素,均會(huì)影響到車輛對(duì)象的提取以及進(jìn)一步清晰化處理。故建立基于不同卡口的視頻樣本庫(kù),進(jìn)行視頻質(zhì)量評(píng)價(jià),采用基于ROI運(yùn)動(dòng)目標(biāo)區(qū)域的視頻質(zhì)量評(píng)價(jià)方法,針對(duì)視頻質(zhì)量評(píng)價(jià)的結(jié)果進(jìn)行對(duì)應(yīng)的視頻預(yù)處理工作,使視頻質(zhì)量評(píng)價(jià)有效關(guān)聯(lián)視頻的預(yù)處理。2、車輛對(duì)象的提取及輪廓清晰化。首先采用了Gamma光照補(bǔ)償、高斯濾波、雙邊濾波、拉普拉斯銳化、直方圖均衡化等預(yù)處理方法,然后對(duì)預(yù)處理后的視頻進(jìn)行混合高斯背景建模提取出目標(biāo)車輛,用基于HSV顏色空間的陰影消除算法,消除陰影得到完整的車輛對(duì)象,最后利用序列幀間運(yùn)動(dòng)補(bǔ)償?shù)姆椒ㄟM(jìn)行輪廓修復(fù),并引入輪廓修復(fù)評(píng)價(jià)函數(shù),控制修復(fù)的合理性和有效性,確保車輛對(duì)象輪廓清晰完整。3、基于車道線的視頻測(cè)速。利用高速公路中車道線已知標(biāo)準(zhǔn)的信息,建立以車道線為基準(zhǔn)的測(cè)速區(qū)域,通過車輛對(duì)測(cè)速區(qū)域進(jìn)出邊的撞邊算法計(jì)算撞邊幀數(shù),根據(jù)視頻幀率換算出時(shí)間,從而計(jì)算出車輛的行駛速度。
[Abstract]:Intelligent Transportation system is the mainstream of traffic system in the future. The combination of advanced traffic control and computer science will efficiently serve human beings and greatly improve the efficiency of supervision and handling of violations. At present, the monitoring and enforcement of vehicles by the regulatory authorities, mainly through the license plate automatic recognition system to identify the vehicle license plate, as evidence of violations of the law to investigate their responsibilities. However, the existing license plate automatic recognition system can not recognize the license plate intentionally, the license plate does not hang and the license plate is used, so it needs to be punished by manual discrimination, which is time-consuming and laborious. Solving this problem has become a hot and difficult problem in intelligent transportation. The research content of this paper is the basic research work in distributed video vehicle identity time and space link retrieval, which provides important basic technical support for video vehicle identity time and space link retrieval. The main research points of this paper are as follows: 1, video sample establishment and quality analysis and evaluation. In view of the different angle of highway bayonet, the different road environment, the frequent change of weather and light, etc., all these factors will affect the extraction of vehicle object and the further clear processing. Therefore, the video sample database based on different bayonets is established to evaluate the video quality, and the video quality evaluation method based on the moving target area of ROI is adopted to carry out the corresponding video preprocessing work according to the results of the video quality evaluation. It makes the video quality evaluation effective correlation video preprocessing. 2, vehicle object extraction and contour clarity. Firstly, Gamma illumination compensation, Gao Si filtering, bilateral filtering, Laplacian sharpening, histogram equalization and other preprocessing methods are used, and then the target vehicle is extracted by hybrid Gao Si background modeling. The shadow elimination algorithm based on HSV color space is used to eliminate the shadow to obtain the complete vehicle object. Finally, the contour restoration is carried out by using the method of motion compensation between sequence frames, and the contour repair evaluation function is introduced to control the rationality and effectiveness of the restoration. Ensure vehicle object profile is clear and complete. 3. Video speed measurement based on lane line. Based on the known standard information of freeway lane line, the speed measuring area based on lane line is established, and the number of frames of collision edge is calculated by the algorithm of vehicle collision edge in and out of the speed measuring area, and the time is converted according to video frame rate. Thus the speed of the vehicle is calculated.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;TP391.41

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本文編號(hào)：1933986