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

  更多相關(guān)文章： 智能交通系統(tǒng) 交通參數(shù)檢測 車流量檢測與統(tǒng)計(jì) 幀差法

【摘要】：隨著國民經(jīng)濟(jì)的快速發(fā)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(shí)現(xiàn)交通信息實(shí)時檢測、共享、交流的智能交通系統(tǒng)(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(shù)具有信息量豐富、設(shè)置靈活、成本低等優(yōu)點(diǎn)。本文對基于視頻的車流量檢測統(tǒng)計(jì)技術(shù)中的檢測與統(tǒng)計(jì)算法進(jìn)行了研究,其主要內(nèi)容包括以下幾個方面:(1)檢測區(qū)域的設(shè)置及幾何校正。首先對視頻數(shù)據(jù)進(jìn)行采集及預(yù)處理,然后在視頻數(shù)據(jù)的首幀中手動設(shè)置車道、以確定檢測區(qū)域,并用幾何變換對目標(biāo)圖像進(jìn)行校正。(2)對運(yùn)動目標(biāo)檢測算法進(jìn)行了分析、改進(jìn)。對比分析了傳統(tǒng)的目標(biāo)檢測方法,針對實(shí)際交通場景的視頻特性,將幀間差分法和背景差分法相結(jié)合實(shí)現(xiàn)運(yùn)動目標(biāo)的檢測,同時通過背景建模的方式對背景進(jìn)行實(shí)時更新,并對這幾個的算法做了改進(jìn),使得檢測目標(biāo)更加完整可靠和準(zhǔn)確。(3)對目標(biāo)特征提取算法進(jìn)行了分析及改進(jìn)。從檢測區(qū)域二值化圖像中提取車輛信息數(shù)據(jù)流,確定出運(yùn)動目標(biāo)區(qū)域的二維邊界,然后依據(jù)目標(biāo)區(qū)域車尾的中心位置選取合適的匹配準(zhǔn)則并統(tǒng)計(jì)車輛數(shù)目。和傳統(tǒng)算法相比不僅算法的執(zhí)行效率高而且車輛可以實(shí)現(xiàn)多車道、跨車道的同時計(jì)數(shù)。仿真實(shí)驗(yàn)結(jié)果表明:本文的算法能夠比較準(zhǔn)確的檢測到經(jīng)過路口的每一輛車輛,同時也可以統(tǒng)計(jì)出一段時間內(nèi)道路的交通流量。車流量的統(tǒng)計(jì)效果比較穩(wěn)定,能夠保持在95%以上的準(zhǔn)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(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

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本文編號：1387507