本文關鍵詞：基于視頻的車流量檢測　出處：《蘭州理工大學》2016年碩士論文　論文類型：學位論文

  更多相關文章： 智能交通系統 交通參數檢測 車流量檢測與統計 幀差法

【摘要】：隨著國民經濟的快速發展,汽車的保有量與日俱增,各種各樣的交通問題也隨之而來,因此建立一種能實現交通信息實時檢測、共享、交流的智能交通系統(ITS)就顯得尤為重要。作為ITS的組成部分,基于視頻的車流量檢測技術具有信息量豐富、設置靈活、成本低等優點。本文對基于視頻的車流量檢測統計技術中的檢測與統計算法進行了研究,其主要內容包括以下幾個方面:(1)檢測區域的設置及幾何校正。首先對視頻數據進行采集及預處理,然后在視頻數據的首幀中手動設置車道、以確定檢測區域,并用幾何變換對目標圖像進行校正。(2)對運動目標檢測算法進行了分析、改進。對比分析了傳統的目標檢測方法,針對實際交通場景的視頻特性,將幀間差分法和背景差分法相結合實現運動目標的檢測,同時通過背景建模的方式對背景進行實時更新,并對這幾個的算法做了改進,使得檢測目標更加完整可靠和準確。(3)對目標特征提取算法進行了分析及改進。從檢測區域二值化圖像中提取車輛信息數據流,確定出運動目標區域的二維邊界,然后依據目標區域車尾的中心位置選取合適的匹配準則并統計車輛數目。和傳統算法相比不僅算法的執行效率高而且車輛可以實現多車道、跨車道的同時計數。仿真實驗結果表明:本文的算法能夠比較準確的檢測到經過路口的每一輛車輛,同時也可以統計出一段時間內道路的交通流量。車流量的統計效果比較穩定,能夠保持在95%以上的準確率。
[Abstract]:With the rapid development of the national economy, the amount of the automobile traffic problems grow with each passing day, then, so the establishment of a real-time detection, traffic information sharing, communication, intelligent transportation system (ITS) is particularly important. As a part of ITS, vehicle flow detection technology of video with abundant information, set up a flexible based on low cost. This paper made a research on detection and statistical algorithm of traffic flow detection in video based on statistical techniques, the main contents include the following aspects: (1) detection region setting and geometric correction. Firstly, acquisition and preprocessing of video data, and then the lane is set manually in the first frame video data, to determine the detection area and the target image is corrected by geometric transformation. (2) of the moving target detection algorithm is analyzed, the comparative analysis of improvement. Target detection system, aiming at the features of actual traffic scenes, the frame difference method and background difference method combined with the detection of moving objects, and through background modeling for real-time updates on the background, and the algorithm has been improved, so that the detection target is more complete and reliable and accurate. (3) the target feature extraction algorithm was analyzed and improved. The detection area binarization extracting vehicle information and data flow image, determine the two-dimensional boundary of target region, and then based on the center of the target area rear to select the appropriate matching criterion and count the number of vehicles. Compared with the traditional algorithm not only algorithm the implementation of high efficiency and can realize multi Lane vehicle, cross lane count. The simulation results show that this algorithm can detect accurately through the intersection to each car car, with The traffic flow can be counted for a period of time. The statistical effect of the traffic flow is stable, and the accuracy of the traffic can be kept above 95%.

【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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本文編號：1387507