本文關(guān)鍵詞：高速公路擁堵事件檢測(cè)中的背景建模及狀態(tài)判別方法研究　出處：《重慶大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 擁堵判別 非參數(shù)核密度 分形維數(shù) 目標(biāo)提取 連通域分析

【摘要】：由于場(chǎng)景的封閉性及行車速度高的特點(diǎn)，發(fā)生在高速公路的交通擁堵嚴(yán)重影響其通行能力，極易引發(fā)二次事故而導(dǎo)致人員傷亡和嚴(yán)重的經(jīng)濟(jì)損失。作為基本的監(jiān)控手段，高速公路關(guān)鍵路段已安裝大量的視頻監(jiān)控裝置，傳統(tǒng)的基于視頻的擁堵檢測(cè)方法由于高速公路場(chǎng)景復(fù)雜、視頻圖像易受環(huán)境影響的問題而難以獲得滿意的結(jié)果。因此，充分利用現(xiàn)有監(jiān)控資源，研究基于視頻的高速公路交通擁堵狀態(tài)判別方法對(duì)提高高速公路通行能力和運(yùn)行安全具有重要的理論和實(shí)際意義。 車輛目標(biāo)分割、交通參數(shù)的提取是交通擁堵判別的基礎(chǔ)，現(xiàn)有基于視頻的交通擁堵狀態(tài)判別對(duì)復(fù)雜環(huán)境下車輛目標(biāo)分割、目標(biāo)不完整時(shí)交通參數(shù)提取以及降低交通擁堵狀態(tài)判別誤檢測(cè)的問題鮮有研究。針對(duì)上述問題，論文在深入分析現(xiàn)有交通擁堵狀態(tài)判別算法的基礎(chǔ)上，重點(diǎn)研究了目標(biāo)提取和狀態(tài)判別，包括復(fù)雜場(chǎng)景下的背景建模、交通參數(shù)提取和降低交通擁堵狀態(tài)判別誤檢測(cè)的問題，最終形成了一套基于視頻的高速公路環(huán)境下的交通擁堵狀態(tài)判別方法。 在車輛目標(biāo)提取方面，針對(duì)高速公路場(chǎng)景復(fù)雜、視頻圖像易受環(huán)境影響的特點(diǎn)，建立了非參數(shù)核密度估計(jì)方法的背景建模，同時(shí)給出了場(chǎng)景光線突變和漸變條件下的背景更新方案。此外，針對(duì)車流密度大而導(dǎo)致獲取的背景圖像質(zhì)量較差的問題，提出了基于分形維數(shù)的初始車流密度檢測(cè)算法。在此基礎(chǔ)上，利用背景差分法和形態(tài)學(xué)的前景去噪方法提取車輛目標(biāo)。實(shí)驗(yàn)結(jié)果表明，提出的背景建模與更新方法能夠判斷初始車流密度的大小和適應(yīng)背景光線的變化，從而提高了提取出的車輛目標(biāo)的準(zhǔn)確性。 在交通擁堵狀態(tài)判別方面，提出了基于模糊C均值的擁堵狀態(tài)判別方法。針對(duì)車輛目標(biāo)檢測(cè)不完整而導(dǎo)致交通參數(shù)獲取不準(zhǔn)確的問題，通過對(duì)常用交通參數(shù)特性的分析，提出了以平均空間占有率和時(shí)間占有率作為交通擁堵判別參數(shù)。在此基礎(chǔ)上，根據(jù)交通狀態(tài)之間具有的模糊性，利用模糊C均值算法來獲取擁堵狀態(tài)的聚類中心，并利用歐式距離來得到當(dāng)前的交通擁堵狀態(tài)。此外，為了降低交通擁堵狀態(tài)的誤檢測(cè)，提出了基于連通域分析的誤檢測(cè)識(shí)別算法和基于投票機(jī)制的擁堵判別方法，進(jìn)一步提高了交通擁堵狀態(tài)判別算法的準(zhǔn)確性。 最后，，綜合上述研究成果，建立了高速公路交通擁堵狀態(tài)判別實(shí)驗(yàn)系統(tǒng)，利用重慶市高速公路監(jiān)控場(chǎng)景視頻，在VC環(huán)境下進(jìn)行實(shí)驗(yàn)驗(yàn)證。實(shí)驗(yàn)結(jié)果表明，論文所提方法能夠獲得較準(zhǔn)確的車輛目標(biāo)，且僅須提取少量交通參數(shù)就能判別交通擁堵狀態(tài)，提高了高速公路復(fù)雜場(chǎng)景下的交通擁堵狀態(tài)判別的準(zhǔn)確性和可靠性。
[Abstract]:Due to the closure of the scene and the characteristics of high speed, traffic congestion on the freeway seriously affects its capacity. It is easy to cause secondary accidents and cause casualties and serious economic losses. As a basic means of monitoring, a large number of video surveillance devices have been installed in key sections of the highway. The traditional video based congestion detection method is difficult to obtain satisfactory results due to the complexity of freeway scene and the vulnerability of video image to environment. Therefore, the existing monitoring resources are fully utilized. It is of great theoretical and practical significance to study the video based identification method for freeway traffic jams to improve highway traffic capacity and operation safety. Vehicle target segmentation and extraction of traffic parameters are the basis of traffic congestion discrimination. The existing video based traffic congestion state discrimination is used to segment vehicle targets in complex environment. The problem of extracting traffic parameters and reducing traffic congestion discrimination error detection when the target is incomplete is rarely studied. In view of the above problems, this paper deeply analyzes the existing traffic congestion discrimination algorithms. The research focuses on object extraction and state discrimination, including background modeling in complex scenarios, traffic parameter extraction and error detection in reducing traffic congestion. Finally, a set of video-based traffic congestion discrimination method is formed. In the aspect of vehicle target extraction, aiming at the complexity of freeway scene and the vulnerability of video image to environment, the background modeling of nonparametric kernel density estimation method is established. At the same time, the background updating scheme under the condition of sudden change of scene light and gradual change is given. In addition, the quality of the background image is poor because of the heavy traffic density. An algorithm for detecting initial vehicle flow density based on fractal dimension is proposed. Based on this, the background difference method and morphological foreground denoising method are used to extract vehicle targets. The proposed background modeling and updating method can judge the initial traffic density and adapt to the change of background light, thus improving the accuracy of the extracted vehicle targets. In the aspect of traffic congestion identification, a method based on fuzzy C-means is proposed, which leads to the inaccurate acquisition of traffic parameters due to the incomplete detection of vehicle targets. Based on the analysis of the characteristics of common traffic parameters, the average space occupation rate and time occupancy rate are used as traffic congestion discrimination parameters, and on this basis, according to the fuzziness between traffic states. The fuzzy C-means algorithm is used to obtain the clustering center of the congestion state, and the Euclidean distance is used to get the current traffic congestion state. In addition, in order to reduce the false detection of the traffic congestion state. An algorithm of error detection and identification based on connected domain analysis and a congestion discrimination method based on voting mechanism are proposed to improve the accuracy of traffic congestion identification algorithm. Finally, based on the above research results, a highway traffic congestion identification experiment system is established. The video of Chongqing expressway monitoring scene is used to verify the experiment in VC environment. The method proposed in this paper can obtain more accurate vehicle targets, and only a small number of traffic parameters can be extracted to identify traffic congestion. The accuracy and reliability of identification of traffic congestion state in complex scene of expressway are improved.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.265

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