【摘要】：基于視頻圖像的車流量統(tǒng)計(jì)系統(tǒng)是智能交通系統(tǒng)的重要研究課題之一,它利用圖像處理和人工智能技術(shù)對攝錄的車流視頻進(jìn)行分析處理,獲取在某個時段中道路上通過的車輛數(shù)目,為智能交通系統(tǒng)提供后續(xù)處理的基本數(shù)據(jù),實(shí)現(xiàn)道路智能化調(diào)度,提高路面資源利用率。基于目標(biāo)跟蹤的車流量統(tǒng)計(jì)方法在實(shí)際應(yīng)用中,由于受到光照的影響經(jīng)常會出現(xiàn)陰影問題,當(dāng)陰影區(qū)域面積較大和車輛間行駛距離較近時,會造成圖像中車輛發(fā)生粘連,從而影響車流量統(tǒng)計(jì)系統(tǒng)的準(zhǔn)確率。因此,車輛陰影消除算法、粘連車輛分割算法是車流量統(tǒng)計(jì)系統(tǒng)的關(guān)鍵技術(shù)。本文在研究車流量統(tǒng)計(jì)過程中常用的陰影消除和粘連分割算法的基礎(chǔ)上,實(shí)現(xiàn)了一個基于目標(biāo)跟蹤的車流量統(tǒng)計(jì)系統(tǒng)。本文主要研究工作如下:1.針對車輛邊緣的遮擋車輛曲線分割算法在分割過程中準(zhǔn)確率低的問題,改進(jìn)了一種基于凹性分析的粘連車輛分割算法。仿真實(shí)驗(yàn)結(jié)果表明,本文的算法與車輛邊緣的遮擋車輛曲線分割算法相比,在分割車輛時具有較高的準(zhǔn)確率,從而可以有效地提高車流量統(tǒng)計(jì)系統(tǒng)的準(zhǔn)確性。2.對常用的陰影消除算法(HSV顏色特征算法、梯度特征算法)進(jìn)行了分析研究。針對上述兩種算法各自消除率低的問題,提出了一種基于HSV顏色特征和梯度特征融合的陰影消除算法。仿真實(shí)驗(yàn)結(jié)果證明,本文所提出的算法具有較高的陰影消除率,在一定程度上提高了車輛檢測的魯棒性。3.在Visual Studio 2010集成開發(fā)環(huán)境下,采用MFC應(yīng)用程序框架及Open CV計(jì)算機(jī)視覺庫,實(shí)現(xiàn)了基于目標(biāo)跟蹤的車流量統(tǒng)計(jì)系統(tǒng)。實(shí)驗(yàn)結(jié)果表明,本文所設(shè)計(jì)的系統(tǒng)具有較好的實(shí)時性,可以在一定程度上克服光照的影響。
[Abstract]:The traffic flow statistics system based on video image is one of the important research topics of intelligent transportation system. It uses image processing and artificial intelligence technology to analyze and process the recorded traffic flow video. The number of vehicles passing through the road in a certain period of time is obtained, the basic data of follow-up processing are provided for the intelligent transportation system, the intelligent dispatching of the road is realized, and the utilization rate of pavement resources is improved. In practical application, the traffic flow statistics method based on target tracking often has shadow problem because of the influence of illumination. when the shadow area is large and the driving distance between vehicles is close, the vehicle adhesion will occur in the image. Thus, the accuracy of traffic flow statistics system is affected. Therefore, vehicle shadow elimination algorithm and adhesion vehicle segmentation algorithm are the key technologies of traffic flow statistics system. Based on the study of shadow elimination and adhesion segmentation algorithms commonly used in traffic flow statistics, a traffic flow statistics system based on target tracking is implemented in this paper. The main research work of this paper is as follows: 1. In order to solve the problem of low accuracy of vehicle curve segmentation algorithm for vehicle edge occlusion, an adhesive vehicle segmentation algorithm based on concave analysis is improved. The simulation results show that compared with the vehicle edge occlusion vehicle curve segmentation algorithm, the proposed algorithm has higher accuracy in vehicle segmentation, which can effectively improve the accuracy of the traffic flow statistics system. 2. The common shadow elimination algorithms (HSV color feature algorithm, gradient feature algorithm) are analyzed and studied. In order to solve the problem of low elimination rate of the above two algorithms, a shadow elimination algorithm based on the fusion of HSV color features and gradient features is proposed. The simulation results show that the algorithm proposed in this paper has a high shadow elimination rate and improves the robustness of vehicle detection to a certain extent. Based on Visual Studio 2010 integrated development environment, a traffic flow statistics system based on target tracking is implemented by using MFC application framework and Open CV computer visual library. The experimental results show that the system designed in this paper has good real-time performance and can overcome the influence of light to a certain extent.
【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】：2017
【分類號】：U491;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 楊濱;陳先意;胡偉峰;;基于陰影檢測模型的圖像拼接盲取證[J];應(yīng)用科學(xué)學(xué)報(bào);2016年05期

2 吳秋爽;;基于計(jì)算機(jī)視覺的智能交通監(jiān)控系統(tǒng)[J];智能城市;2016年03期

3 魯學(xué)軍;武鵬達(dá);郭旭東;;二值形態(tài)閉運(yùn)算在優(yōu)質(zhì)耕地集中連片劃定中的應(yīng)用[J];中國圖象圖形學(xué)報(bào);2016年02期

4 劉燕妮;張貴倉;安靜;;基于數(shù)學(xué)形態(tài)學(xué)的雙直方圖均衡化圖像增強(qiáng)算法[J];計(jì)算機(jī)工程;2016年01期

5 王同;彭祺;屠禮芬;;基于OpenCV的運(yùn)動目標(biāo)檢測軟件實(shí)現(xiàn)[J];軟件導(dǎo)刊;2015年12期

6 韓延彬;郭曉鵬;魏延文;李恒建;;RGB和HSI顏色空間的一種改進(jìn)的陰影消除算法[J];智能系統(tǒng)學(xué)報(bào);2015年05期

7 沈陽;宓超;鳳宇飛;;形態(tài)學(xué)開運(yùn)算在車型圖像去噪中的應(yīng)用[J];中國科技信息;2015年18期

8 戴橙;陳勝;;改進(jìn)的分水嶺算法用于X光醫(yī)學(xué)圖像分割[J];電子科技;2015年06期

9 魏景璇;魯燃;張艷輝;;基于動態(tài)閾值和命名實(shí)體的雙重過濾話題追蹤[J];計(jì)算機(jī)應(yīng)用研究;2015年04期

10 王平;齊帥;張力;;一種強(qiáng)粘連與畸變巨噬細(xì)胞圖像的分割方法[J];計(jì)算機(jī)應(yīng)用研究;2014年12期

相關(guān)碩士學(xué)位論文 前9條

1 黃霞;基于視覺的交通車流量統(tǒng)計(jì)[D];湘潭大學(xué);2016年

2 苗永祿;基于視頻圖像處理的車輛檢測與跟蹤方法研究[D];長安大學(xué);2014年

3 田輝;基于視頻的車流量統(tǒng)計(jì)算法研究[D];大連海事大學(xué);2012年

4 墨芹;運(yùn)動車輛視頻檢測與車流量檢測方法的研究[D];中南大學(xué);2012年

5 田立明;基于視頻的高速公路車流量檢測系統(tǒng)研究與實(shí)現(xiàn)[D];山東大學(xué);2012年

6 華桃桃;人臉識別中基于子空間的特征提取方法研究[D];重慶大學(xué);2012年

7 曾詩;視頻序列中的運(yùn)動目標(biāo)分類算法研究[D];南京郵電大學(xué);2012年

8 蘇書杰;視頻檢測系統(tǒng)中的背景檢測及更新算法研究[D];長安大學(xué);2009年

9 王晉;基于運(yùn)動目標(biāo)跟蹤的車流量統(tǒng)計(jì)技術(shù)研究[D];昆明理工大學(xué);2009年

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