【摘要】：隨著經(jīng)濟(jì)的快速發(fā)展,各個(gè)城市的汽車(chē)數(shù)量不斷增加,道路的交通狀況越發(fā)的復(fù)雜,實(shí)現(xiàn)交通狀況的準(zhǔn)確判別是解決道路擁堵問(wèn)題的基礎(chǔ)。道路監(jiān)控系統(tǒng)的普及、圖像處理與模式識(shí)別等技術(shù)的發(fā)展,使得基于視頻的交通特征參數(shù)的提取實(shí)現(xiàn)交通狀況的判別成為當(dāng)前研究的熱點(diǎn)。在實(shí)際的場(chǎng)景中道路信息系統(tǒng)的故障在所難免,容易造成道路交通流量數(shù)據(jù)的丟失,實(shí)現(xiàn)這些數(shù)據(jù)的修復(fù)顯得尤為重要。為了解決道路狀況判別這一問(wèn)題,本文通過(guò)對(duì)道路交通視頻的處理,獲得道路交通特征參數(shù),提出了一種基于核函數(shù)模糊C均值聚類(KFCM)的交通擁堵判別方法,同時(shí)將時(shí)空壓縮感知壓縮感知應(yīng)用于道路交通流量數(shù)據(jù)的修復(fù)過(guò)程中。論文的主要工作如下:從實(shí)時(shí)交通視頻中獲得道路交通特征參數(shù),首先要實(shí)現(xiàn)運(yùn)動(dòng)車(chē)輛的目標(biāo)檢測(cè)。本文對(duì)傳統(tǒng)的像素級(jí)Vibe目標(biāo)檢測(cè)算法進(jìn)行了改進(jìn),提出了一種基于閾值的自適應(yīng)Vibe目標(biāo)檢測(cè)算法。針對(duì)檢測(cè)中存在的鬼影,引入了基于Otsu閾值的鬼影抑制方法,將單個(gè)像素點(diǎn)的背景判別與整幅圖像的特征相結(jié)合。為了更好地適應(yīng)前景目標(biāo)運(yùn)動(dòng)狀況變化較大的情況,根據(jù)前景目標(biāo)質(zhì)心的運(yùn)動(dòng)速度,自適應(yīng)的調(diào)整背景的更新速度。實(shí)驗(yàn)證明,本文的改進(jìn)算法,能夠快速有效的抑制鬼影,同時(shí)提高了目標(biāo)檢測(cè)的準(zhǔn)確性和魯棒性。其次,本文提出了一種基于KFCM的交通擁堵判別方法。交通擁堵的判別采用道路空間占道比、車(chē)流量以及道路宏觀光流速度三個(gè)參數(shù)。對(duì)交通視頻通過(guò)多幀融合進(jìn)行道路的檢測(cè),計(jì)算前景目標(biāo)像素個(gè)數(shù)與道路像素個(gè)數(shù)的比值獲得道路空間占道;通過(guò)虛擬線圈法與Vibe算法結(jié)合統(tǒng)計(jì)車(chē)流量;融合了Harris角點(diǎn)檢測(cè)算法以及H-S光流算法計(jì)算了整個(gè)車(chē)道的宏觀光流速度。在此基礎(chǔ)上,根據(jù)交通狀態(tài)之間具有的模糊性,采用KFCM算法尋找交通狀態(tài)的聚類中心,建立交通擁堵判別器,最后通過(guò)計(jì)算歐氏距離得到當(dāng)前的交通擁堵?tīng)顟B(tài)。實(shí)驗(yàn)證明,本文提出的方法能夠快速準(zhǔn)確的進(jìn)行道路擁堵?tīng)顟B(tài)的判別。最后,視頻交通特征參數(shù)獲取過(guò)程中交通流量參數(shù)可能丟失,道路交通流量的結(jié)構(gòu)特性使其具有一定的冗余性和可壓縮性,因此可將時(shí)空壓縮感知理論應(yīng)用于交通流量參數(shù)的修復(fù)中。本文構(gòu)造了道路網(wǎng)絡(luò)的交通流量矩陣,并結(jié)合道路流量的低秩性和時(shí)間-空間相關(guān)性的特點(diǎn),提出了交通流量參數(shù)的時(shí)間相關(guān)矩陣和空間相關(guān)矩陣的構(gòu)造方法,并利用近似矩陣對(duì)缺失元素進(jìn)行插值重構(gòu)實(shí)現(xiàn)流量數(shù)據(jù)的修復(fù)。該方法能夠準(zhǔn)確有效的修復(fù)缺失的交通流量參數(shù)。
[Abstract]:With the rapid development of economy, the number of cars in each city is increasing, and the traffic situation is becoming more and more complicated. The accurate identification of traffic condition is the basis of solving the problem of road congestion. With the popularization of road monitoring system and the development of image processing and pattern recognition technology, the extraction of traffic feature parameters based on video has become a hot research topic. In the actual scenario, the failure of road information system is inevitable, which can easily lead to the loss of road traffic flow data, so it is particularly important to realize the repair of these data. In order to solve the problem of road condition discrimination, the road traffic characteristic parameters are obtained by processing road traffic video, and a traffic congestion discrimination method based on kernel function fuzzy C-means clustering (KFCM) is proposed. At the same time, space-time compression perception is applied to the restoration of road traffic flow data. The main work of this paper is as follows: firstly, the target detection of moving vehicles should be realized by obtaining the characteristic parameters of road traffic from real-time traffic video. In this paper, the traditional pixel level Vibe target detection algorithm is improved, and an adaptive Vibe target detection algorithm based on threshold is proposed. In view of the existence of ghosts in the detection, a Otsu threshold based ghost image suppression method is introduced, which combines the background discrimination of a single pixel with the features of the whole image. In order to better adapt to the situation where the moving state of the foreground target changes greatly, the updating speed of the background is adjusted adaptively according to the velocity of the centroid of the foreground target. Experimental results show that the improved algorithm can suppress ghost images quickly and effectively, and improve the accuracy and robustness of target detection. Secondly, this paper proposes a traffic congestion discrimination method based on KFCM. Traffic congestion is judged by three parameters: road space ratio, vehicle flow rate and road macroscopic light flow speed. Traffic video is detected by multi-frame fusion, the ratio of foreground pixels to road pixels is calculated, and the traffic flow is calculated by virtual coil method and Vibe algorithm. Harris corner detection algorithm and H-S optical flow algorithm are combined to calculate the macro optical flow velocity of the whole driveway. On this basis, according to the fuzziness between traffic states, the KFCM algorithm is used to find the clustering center of traffic state, and the traffic congestion discriminator is established. Finally, the current traffic congestion state is obtained by calculating Euclidean distance. Experimental results show that the proposed method can quickly and accurately distinguish the traffic congestion. Finally, the traffic flow parameters may be lost in the process of obtaining video traffic characteristic parameters, and the structural characteristics of road traffic flow make it redundant and compressible. Therefore, the theory of space-time compression sensing can be applied to the restoration of traffic flow parameters. In this paper, the traffic flow matrix of road network is constructed, and combined with the characteristics of low rank and time-space correlation of road flow, the method of constructing time correlation matrix and spatial correlation matrix of traffic flow parameters is proposed. And the approximate matrix is used to interpolate and reconstruct the missing elements to repair the traffic data. This method can accurately and effectively repair the missing traffic flow parameters.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.265

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相關(guān)期刊論文 前1條

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本文編號(hào)：2351294