基于道路監(jiān)控視頻的交通擁堵判別方法研究
發(fā)布時間：2018-11-23 10:39
【摘要】：隨著經濟的快速發(fā)展,各個城市的汽車數量不斷增加,道路的交通狀況越發(fā)的復雜,實現(xiàn)交通狀況的準確判別是解決道路擁堵問題的基礎。道路監(jiān)控系統(tǒng)的普及、圖像處理與模式識別等技術的發(fā)展,使得基于視頻的交通特征參數的提取實現(xiàn)交通狀況的判別成為當前研究的熱點。在實際的場景中道路信息系統(tǒng)的故障在所難免,容易造成道路交通流量數據的丟失,實現(xiàn)這些數據的修復顯得尤為重要。為了解決道路狀況判別這一問題,本文通過對道路交通視頻的處理,獲得道路交通特征參數,提出了一種基于核函數模糊C均值聚類(KFCM)的交通擁堵判別方法,同時將時空壓縮感知壓縮感知應用于道路交通流量數據的修復過程中。論文的主要工作如下:從實時交通視頻中獲得道路交通特征參數,首先要實現(xiàn)運動車輛的目標檢測。本文對傳統(tǒng)的像素級Vibe目標檢測算法進行了改進,提出了一種基于閾值的自適應Vibe目標檢測算法。針對檢測中存在的鬼影,引入了基于Otsu閾值的鬼影抑制方法,將單個像素點的背景判別與整幅圖像的特征相結合。為了更好地適應前景目標運動狀況變化較大的情況,根據前景目標質心的運動速度,自適應的調整背景的更新速度。實驗證明,本文的改進算法,能夠快速有效的抑制鬼影,同時提高了目標檢測的準確性和魯棒性。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、車流量以及道路宏觀光流速度三個參數。對交通視頻通過多幀融合進行道路的檢測,計算前景目標像素個數與道路像素個數的比值獲得道路空間占道;通過虛擬線圈法與Vibe算法結合統(tǒng)計車流量;融合了Harris角點檢測算法以及H-S光流算法計算了整個車道的宏觀光流速度。在此基礎上,根據交通狀態(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,最后通過計算歐氏距離得到當前的交通擁堵狀態(tài)。實驗證明,本文提出的方法能夠快速準確的進行道路擁堵狀態(tài)的判別。最后,視頻交通特征參數獲取過程中交通流量參數可能丟失,道路交通流量的結構特性使其具有一定的冗余性和可壓縮性,因此可將時空壓縮感知理論應用于交通流量參數的修復中。本文構造了道路網絡的交通流量矩陣,并結合道路流量的低秩性和時間-空間相關性的特點,提出了交通流量參數的時間相關矩陣和空間相關矩陣的構造方法,并利用近似矩陣對缺失元素進行插值重構實現(xiàn)流量數據的修復。該方法能夠準確有效的修復缺失的交通流量參數。
[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.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.265
本文編號：2351294
[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.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.265
【參考文獻】
相關期刊論文 前1條
1 徐健銳;李星毅;施化吉;;處理缺失數據的短時交通流預測模型[J];計算機應用;2010年04期
，本文編號：2351294
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