本文選題：運(yùn)動(dòng)車輛檢測(cè)　切入點(diǎn)：車輛跟蹤　出處：《安徽大學(xué)》2014年碩士論文

【摘要】：智能交通系統(tǒng)(Intelligent Transportation System, ITS)成為如今交通系統(tǒng)發(fā)展的方向。其中,利用視頻監(jiān)控技術(shù),高效地對(duì)運(yùn)動(dòng)車輛進(jìn)行檢測(cè)與跟蹤成為智能交通系統(tǒng)研究中的一個(gè)熱點(diǎn)和難點(diǎn)。它是判斷車輛行為,解決道路擁堵等問(wèn)題的前提。本文主要研究路口單一攝像機(jī)固定的情況下,監(jiān)控視頻中運(yùn)動(dòng)車輛的自動(dòng)檢測(cè)、跟蹤技術(shù),對(duì)原有算法進(jìn)行了一些改進(jìn),完成了相應(yīng)程序的編寫(xiě)。 在實(shí)際視頻智能監(jiān)控中,能否實(shí)時(shí)的進(jìn)行計(jì)算機(jī)處理成為衡量算法實(shí)用價(jià)值的一個(gè)重要指標(biāo)。本文從實(shí)際應(yīng)用的角度,深入研究了運(yùn)動(dòng)車輛檢測(cè)與跟蹤算法,主要的工作可概況為以下幾點(diǎn)： (1)分析比較運(yùn)動(dòng)目標(biāo)檢測(cè)算法,最終選用ViBe算法作為本文運(yùn)動(dòng)車輛檢測(cè)算法。針對(duì)ViBe易產(chǎn)生鬼影的特點(diǎn),改進(jìn)了ViBe算法,實(shí)驗(yàn)表明改進(jìn)的算法能使鬼影更快速地消除。 (2)針對(duì)運(yùn)動(dòng)車輛陰影對(duì)目標(biāo)檢測(cè)的影響,通過(guò)改進(jìn)得到了在灰度空間下閾值能自適應(yīng)更新的陰影去除算法。通過(guò)實(shí)驗(yàn),驗(yàn)證了該方法具有較好的去陰影效果。 (3)研究目標(biāo)跟蹤的一般方法,從實(shí)時(shí)性、準(zhǔn)確性上得出所需的車輛跟蹤算法。最終改進(jìn)得到了一種窗口受限的聯(lián)合Camshift與Kalman濾波的車輛跟蹤算法,實(shí)驗(yàn)取得了較好的效果。 (4)編程實(shí)現(xiàn)了多口標(biāo)運(yùn)動(dòng)車輛檢測(cè)與跟蹤。結(jié)合OpenCV編寫(xiě)程序,使用基于檢測(cè)線觸發(fā)的機(jī)制實(shí)現(xiàn)了車輛的計(jì)數(shù),多目標(biāo)的跟蹤,完成了車輛的標(biāo)記以及運(yùn)動(dòng)軌跡的自動(dòng)繪制。
[Abstract]:Intelligent Transportation system (ITSs) has become the developing direction of transportation system nowadays. Efficient detection and tracking of moving vehicles has become a hot and difficult point in the research of intelligent transportation system. It is the premise of judging vehicle behavior and solving road congestion problems. The automatic detection and tracking technology of moving vehicles in the surveillance video, the original algorithm has been improved, and the corresponding program has been written. In the actual video intelligent surveillance, it is an important index to measure the practical value of the algorithm by computer processing in real time. In this paper, the moving vehicle detection and tracking algorithm is deeply studied from the perspective of practical application. The main work can be summarized as follows:. 1) analyzing and comparing the moving target detection algorithms, and finally choosing ViBe algorithm as the moving vehicle detection algorithm in this paper. According to the characteristics of ViBe, the ViBe algorithm is improved. The experiment shows that the improved algorithm can eliminate the ghost image more quickly. 2) aiming at the influence of moving vehicle shadow on target detection, a shadow removal algorithm with adaptive updating threshold in gray space is proposed, and the experimental results show that the method has better shadow removal effect. 3) the general method of target tracking is studied, and the needed vehicle tracking algorithm is obtained from real-time and accuracy. Finally, a window-constrained vehicle tracking algorithm with combined Camshift and Kalman filtering is obtained, and the experimental results are satisfactory. Combined with the OpenCV program, the vehicle count and multi-target tracking are realized by using the mechanism based on the detection line trigger, and the vehicle marking and the automatic drawing of the moving track are completed.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TP391.41

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本文編號(hào)：1653307