【摘要】：伴隨著中國進(jìn)入汽車時(shí)代，交通事故也增多起來，由此帶來了巨大的經(jīng)濟(jì)損失。因此，有效的對交叉口或路段進(jìn)行交通安全性評價(jià)成為一個(gè)重要的研究課題。由于交通事故的后發(fā)性，用交通事故評價(jià)交通安全，在實(shí)時(shí)性和準(zhǔn)確性方面稍顯不足。因此，基于交通沖突的交通安全評價(jià)方法得到了廣泛的研究。 雖然基于交通沖突的方法能夠較好的評價(jià)交通安全性，但是目前的交通沖突的采集方法主要是通過人工觀察的手段，這種方法實(shí)時(shí)性差且耗時(shí)費(fèi)力，由此帶來一些測量上的不便。因此本文提出了基于視頻的交通沖突自動判別技術(shù)，該技術(shù)能夠自動判別交通沖突的發(fā)生，，克服了基于人工觀測方法的諸多弊端。 目前基于視頻的交通沖突自動判別系統(tǒng)的研究存在著諸多問題，如目標(biāo)檢測不準(zhǔn)確，跟蹤效果不好，沖突判別正確率不高等。因此本文針對目前交通沖突自動判別系統(tǒng)研究中存在的問題，做出了深入的研究。主要工作如下： （1）基于背景差分的目標(biāo)檢測算法 目標(biāo)檢測是進(jìn)行交通沖突自動判別的第一步，目標(biāo)檢測的準(zhǔn)確程度直接決定著沖突判別的準(zhǔn)確程度。本文對比了現(xiàn)有各個(gè)目標(biāo)檢測算法的優(yōu)劣，最后決定采用背景差分算法提取目標(biāo)。該算法首先利用背景初始化算法提取背景，并且利用背景更新模型更新背景。然后利用背景差分算法得到二值化前景圖像，并用連通區(qū)域標(biāo)定算法得到各個(gè)前景目標(biāo)。最后，利用目標(biāo)分類算法得到目標(biāo)的分類，實(shí)現(xiàn)目標(biāo)檢測。通過實(shí)驗(yàn)分析，本算法取得了良好的檢測效果。 （2）基于在線學(xué)習(xí)的目標(biāo)跟蹤算法 接著要進(jìn)行目標(biāo)跟蹤，本文提出了改進(jìn)的在線增強(qiáng)跟蹤算法。由于原始的在線增強(qiáng)跟蹤算法存在著實(shí)時(shí)性差和對左轉(zhuǎn)目標(biāo)漂移的問題，因此本文對其進(jìn)行了改進(jìn)。首先，本文提出了一個(gè)級聯(lián)分類器提高跟蹤速度，然后提出了一個(gè)主方向模型改進(jìn)跟蹤效果，解決了跟蹤中存在的漂移問題，最后提出了目標(biāo)位置預(yù)測模型減少搜索區(qū)域，進(jìn)一步提高跟蹤的實(shí)時(shí)性。通過實(shí)驗(yàn)分析，本文改進(jìn)的在線增強(qiáng)跟蹤算法較傳統(tǒng)在線增強(qiáng)算法跟蹤速度快，跟蹤精度高。 （3）交通沖突自動判別算法 在獲取了目標(biāo)檢測區(qū)域內(nèi)的所有目標(biāo)的軌跡序列和速度序列數(shù)據(jù)后，就要進(jìn)行實(shí)時(shí)自動的交通判別。首先本文建立了基于臨界距離的交通沖突判別模型，能夠?qū)煌_突進(jìn)行判別。然后基于視頻處理技術(shù)，建立了交通沖突的判別流程。將傳統(tǒng)檢測方法和本文基于視頻的自動判別方法進(jìn)行對比實(shí)驗(yàn)，結(jié)果表明：本文所提出的方法判別速度更快，準(zhǔn)確度更高。 總而言之，本文進(jìn)一步深化了對自動交通沖突判別領(lǐng)域的研究，所提出的算法與現(xiàn)有方法相比更具實(shí)時(shí)性和準(zhǔn)確性，能夠?yàn)榻徊婵诨蚵范伟踩栽u價(jià)服務(wù)，具有重要的理論與實(shí)用價(jià)值。
[Abstract]:With China entering the automobile age, traffic accidents have also increased, which has brought huge economic losses. Therefore, it is an important research topic to evaluate the traffic safety of intersection or section effectively. Due to the lateness of traffic accidents, the evaluation of traffic safety by traffic accidents is a little insufficient in real time and accuracy. Therefore, traffic safety evaluation method based on traffic conflict has been widely studied. Although the method based on traffic conflict can better evaluate the traffic safety, the current traffic conflict collection method is mainly through the means of manual observation, this method is poor real-time and time-consuming. This brings some inconvenience in measurement. Therefore, this paper proposes a video based automatic discrimination technique for traffic conflicts, which can automatically distinguish the occurrence of traffic conflicts and overcome many disadvantages of artificial observation methods. At present, there are many problems in the research of traffic conflict automatic discriminant system based on video, such as inaccurate target detection, poor tracking effect, low accuracy of conflict discrimination and so on. Therefore, this paper makes an in-depth study on the existing problems in the research of traffic conflict automatic discrimination system. The main work is as follows: (1) Target detection algorithm based on background difference is the first step of automatic traffic conflict discrimination. The accuracy of target detection directly determines the accuracy of conflict discrimination. This paper compares the advantages and disadvantages of the existing target detection algorithms, and finally decides to use background differential algorithm to extract the target. Firstly, the background initialization algorithm is used to extract the background, and the background update model is used to update the background. Then the background difference algorithm is used to obtain the binary foreground image and the connected region calibration algorithm is used to obtain each foreground target. Finally, the target classification algorithm is used to achieve target detection. Through experimental analysis, the algorithm has achieved a good detection effect. (2) the target tracking algorithm based on online learning is proposed in this paper. Since the original on-line enhanced tracking algorithm has the problems of poor real-time performance and drift to the left turn target, this paper improves the algorithm. Firstly, a cascade classifier is proposed to improve the tracking speed, then a main direction model is proposed to improve the tracking effect, which solves the drift problem in tracking. Finally, the target location prediction model is proposed to reduce the search area. Further improve the real-time tracking. Through the experimental analysis, the improved on-line enhancement tracking algorithm is faster than the traditional on-line enhancement algorithm, and the tracking accuracy is high. (3) the traffic conflict automatic discriminant algorithm will carry on the real-time automatic traffic discrimination after obtaining the track sequence and velocity sequence data of all the targets in the target detection area. Firstly, a traffic conflict discriminant model based on critical distance is established, which can distinguish traffic conflict. Then, based on the video processing technology, the traffic conflict identification process is established. By comparing the traditional detection method with the automatic discriminant method based on video in this paper, the results show that the method proposed in this paper is faster and more accurate. In a word, this paper further deepens the research on the field of automatic traffic conflict discrimination. Compared with the existing methods, the proposed algorithm is more real-time and accurate, and can serve for the safety evaluation of intersections or sections. It has important theoretical and practical value.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.265;U495

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 藺志青,郭軍;貝葉斯分類器在手寫漢字識別中的應(yīng)用[J];電子學(xué)報(bào);2002年12期

2 王臺生;;基于HMM的背景建模初始化算法研究[J];硅谷;2011年19期

3 羅石貴,周偉;路段交通沖突技術(shù)研究[J];公路交通科技;2001年01期

4 顧國慶,許彥冰;數(shù)字圖像區(qū)域標(biāo)定的方法[J];上海理工大學(xué)學(xué)報(bào);2001年04期

5 李志慧;張長海;曲昭偉;魏巍;王殿海;;交通流視頻檢測中背景初始化算法[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2008年01期

6 萬纓;韓毅;盧漢清;;運(yùn)動目標(biāo)檢測算法的探討[J];計(jì)算機(jī)仿真;2006年10期

7 盧官明;謝雙;;自適應(yīng)背景更新及運(yùn)動目標(biāo)檢測算法[J];南京郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年03期

8 ;Traficon視頻檢測的世界級參考標(biāo)準(zhǔn)——TrafiCam一體化路口視頻車輛檢測器[J];中國交通信息產(chǎn)業(yè);2007年02期

9 羅石貴,周偉;路段交通沖突的調(diào)查技術(shù)[J];長安大學(xué)學(xué)報(bào)(自然科學(xué)版);2003年01期

10 李強(qiáng);趙亦工;高永晶;;基于背景差分的動目標(biāo)檢測算法研究[J];遙測遙控;2007年06期

相關(guān)博士學(xué)位論文 前4條

1 曲昭偉;混合交通視頻檢測算法研究[D];吉林大學(xué);2009年

2 胡宏宇;基于視頻處理的交通事件識別方法研究[D];吉林大學(xué);2010年

3 曾勇;廣義近鄰模式分類研究[D];上海交通大學(xué);2009年

4 江晟;混合交通視頻檢測關(guān)鍵技術(shù)研究[D];吉林大學(xué);2013年

本文編號：2393164