本文選題：智能交通 + 運(yùn)動(dòng)目標(biāo)提取　； 參考：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：違章車輛檢測(cè)與抓拍是智能交通系統(tǒng)中的重要組成部分，在約束司機(jī)行為、減少車輛違章與交通事故、提高交通效率等方面發(fā)揮著重大作用。本文針對(duì)目前車輛越、軋線車道線與違停兩種違章行為的監(jiān)管系統(tǒng)范圍小、檢測(cè)與抓拍成功率較低等問題，研究一種新的基于高速球攝像機(jī)的大范圍監(jiān)管系統(tǒng)。 首先，根據(jù)軋線、違停監(jiān)管應(yīng)用需求分析，設(shè)計(jì)了基于高速球攝像機(jī)的系統(tǒng)總體硬件框架、工作原理和算法流程，以增大監(jiān)管范圍和提高可靠性。 其次，根據(jù)軋線、違停監(jiān)管應(yīng)用需求和系統(tǒng)可靠性問題，對(duì)現(xiàn)有高速球攝像機(jī)缺少角度反饋這一缺陷，將角度編碼器增設(shè)在其內(nèi)部云臺(tái)的兩轉(zhuǎn)動(dòng)軸上，構(gòu)建了閉環(huán)運(yùn)動(dòng)控制系統(tǒng)，并研究了基于ARM的測(cè)控硬件系統(tǒng)，以此為基礎(chǔ)，建立抓拍測(cè)控模型。 然后，為了提高從道路背景中提取出的車輛目標(biāo)準(zhǔn)確性，分別研究了基于有參與無參背景模型的前景檢測(cè)算法，通過理論與實(shí)驗(yàn)研究了現(xiàn)有算法的優(yōu)劣，將一種歷史像素序列與ViBe相結(jié)合，做背景建模，驗(yàn)證實(shí)驗(yàn)表明，與現(xiàn)有多種背景建模算法相比，本文算法的目標(biāo)提取準(zhǔn)確率較高，誤檢率較低，且對(duì)初始化時(shí)產(chǎn)生的偽前景有良好的抑制能力。 此外，針對(duì)現(xiàn)有圖像測(cè)距算法過程復(fù)雜、操作繁瑣等問題，設(shè)計(jì)了基于相機(jī)成像原理和車道等寬線的標(biāo)定方法，分別實(shí)現(xiàn)了縱向（車道方向）與橫向（車道垂直方向）測(cè)距，實(shí)驗(yàn)結(jié)果表明，本文方法實(shí)現(xiàn)簡(jiǎn)單，目標(biāo)位置信息獲取準(zhǔn)確，測(cè)量誤差均在15%以下，較好的達(dá)到了本系統(tǒng)動(dòng)態(tài)抓拍要求。 最后，針對(duì)本系統(tǒng)的大范圍監(jiān)管需要相機(jī)配合云臺(tái)、鏡頭動(dòng)作才可以抓拍到不同位置違章車輛高清圖像的問題，本文結(jié)合抓拍測(cè)控模型，設(shè)計(jì)了動(dòng)態(tài)抓拍方法。為了精確判斷目標(biāo)車輛是否軋線，設(shè)計(jì)一種根據(jù)車輛位置與越線像素比例的軋線判定算法，大大降低非車輛物體引起的誤判定，并針對(duì)車輛違停設(shè)計(jì)違停檢測(cè)算法。整合以上內(nèi)容，構(gòu)建車輛軋線與違停監(jiān)管系統(tǒng)，經(jīng)多個(gè)實(shí)際交通路段試點(diǎn)運(yùn)行，，本系統(tǒng)取得了良好的監(jiān)管效果。
[Abstract]:The detection and capture of illegal vehicles is an important part of Intelligent Transportation system (its), which plays an important role in restricting drivers' behavior, reducing vehicle violations and traffic accidents, improving traffic efficiency and so on. Aiming at the problems of small scope of supervision system and low success rate of detection and capture of two kinds of illegal behaviors such as vehicle crossing, rolling lane line and illegal parking, this paper studies a new kind of large-scale supervision system based on high-speed ball camera. Firstly, according to the requirement analysis of rolling line and disobeying stop supervision application, the overall hardware frame, working principle and algorithm flow of the system based on high-speed ball camera are designed to increase the scope of supervision and increase the reliability. Secondly, according to the problems of the rolling line, the application requirement of stop control and the reliability of the system, the angle encoder is added to the two rotating shafts of the inside of the cloud head, because the existing high-speed ball camera lacks angle feedback. The closed-loop motion control system is constructed, and the hardware system of measurement and control based on arm is studied. Then, in order to improve the accuracy of the vehicle target extracted from the road background, the foreground detection algorithm based on the participating non-parametric background model is studied, and the advantages and disadvantages of the existing algorithms are studied through theory and experiment. A kind of historical pixel sequence is combined with Vibe to model the background. The experimental results show that compared with the existing background modeling algorithms, the proposed algorithm has higher accuracy and lower false detection rate. And it has a good ability to suppress the pseudo foreground of initialization. In addition, aiming at the complicated process and complicated operation of the existing image ranging algorithms, a calibration method based on camera imaging principle and lane isobath is designed, which realizes longitudinal (lane direction) and lateral (lane vertical) ranging, respectively. The experimental results show that the method is simple, the target position information is accurate and the measurement error is less than 15%, which meets the requirement of dynamic capture of the system. Finally, aiming at the problem that the camera is needed to cooperate with the cloud head and the camera can capture the high-definition image of the vehicle in different positions, this paper designs a dynamic capture method based on the measurement and control model. In order to accurately judge whether the target vehicle is rolling line, a rolling line judging algorithm based on the ratio of vehicle position and crossing pixel is designed, which greatly reduces the misjudgment caused by non-vehicle object, and the algorithm of illegal stop detection is designed for vehicle illegal stop design. By integrating the above contents and constructing the vehicle rolling line and disobeying stop supervision system, the system has achieved a good supervision effect through the pilot operation of many actual traffic sections.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495

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