本文選題：兩輪車(chē)輛　切入點(diǎn)：前景提取　出處：《江西理工大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：交通流的數(shù)據(jù)分析和研究是智能交通系統(tǒng)研究的重要組成部分,對(duì)于交通系統(tǒng)的安全、便捷的運(yùn)行不言而喻。作為智能交通系統(tǒng)一部分的車(chē)輛檢測(cè)也因此成為了研究的熱點(diǎn)和重點(diǎn),并取得了很多廣泛應(yīng)用的成果。本文從車(chē)輛檢測(cè)的方向出發(fā),結(jié)合國(guó)內(nèi)外的研究現(xiàn)狀以及國(guó)內(nèi)的交通流狀況,使用圖像處理和機(jī)器學(xué)習(xí)的方法對(duì)兩輪車(chē)輛的檢測(cè)技術(shù)進(jìn)行研究,采用基于模板匹配和預(yù)檢測(cè)結(jié)合機(jī)器學(xué)習(xí)的方法進(jìn)行兩輪車(chē)輛檢測(cè),具體的研究?jī)?nèi)容是:(1)研究和總結(jié)了國(guó)內(nèi)外對(duì)普通車(chē)輛以及兩輪車(chē)輛檢測(cè)技術(shù),并結(jié)合實(shí)際的試驗(yàn)場(chǎng)景,對(duì)傳統(tǒng)的車(chē)輛檢測(cè)技術(shù)(如地磁線圈、超聲紅外傳感等)和基于視頻的車(chē)輛檢測(cè)技術(shù)(如光流法、幀差法、背景差法)等技術(shù)的局限性、安裝便捷性、數(shù)據(jù)處理的直觀性進(jìn)行分析和對(duì)比。(2)采用前景中兩輪車(chē)輛的均值模板對(duì)兩輪車(chē)輛進(jìn)行檢測(cè)。首先對(duì)常采用圖像消噪技術(shù)如中值濾波、高斯濾波進(jìn)行實(shí)驗(yàn)分析說(shuō)明,通過(guò)對(duì)幀差法獲得的前景信息進(jìn)行多次形態(tài)學(xué)的膨脹處理和混合高斯模型的前景信息進(jìn)行與操作,以獲得更完整、噪聲信息更少的前景。使用邊緣檢測(cè)的方法獲得運(yùn)動(dòng)圖像中的車(chē)輛邊緣信息,并利用前景中兩輪車(chē)輛的均值獲得模板,并反饋到前景中,與前景中的動(dòng)態(tài)目標(biāo)進(jìn)行模板匹配,檢測(cè)兩輪車(chē)輛。利用車(chē)輛質(zhì)心軌跡分析的方法對(duì)檢測(cè)的車(chē)輛進(jìn)行計(jì)數(shù)。(3)運(yùn)用混合高斯模型和Ada Boost算法進(jìn)行車(chē)輛檢測(cè)。檢測(cè)步驟包括:利用小汽車(chē)和兩輪車(chē)輛的形狀特征的不同性進(jìn)行預(yù)檢測(cè),使用預(yù)先獲取的正樣本和負(fù)樣本以及Ada Boost機(jī)器學(xué)習(xí)的方法對(duì)樣本的LBP、HAAR、HOG特征分別進(jìn)行分類(lèi)器的訓(xùn)練。并使用分類(lèi)器在預(yù)檢測(cè)的窗口上進(jìn)行兩輪車(chē)輛的檢測(cè),通過(guò)訓(xùn)練時(shí)間以及訓(xùn)練得到的分類(lèi)器在視頻序列中的檢測(cè)的正確率的分析,得出最符合本文環(huán)境的檢測(cè)特征,即LBP特征。實(shí)驗(yàn)表明,本文提出的在預(yù)檢測(cè)的基礎(chǔ)上使用機(jī)器學(xué)習(xí)進(jìn)行兩輪車(chē)輛檢測(cè)的方法可以明顯加快檢測(cè)速度,并有效降低誤檢率。
[Abstract]:Traffic flow data analysis and research is an important part of intelligent transportation system research. The convenient operation is self-evident. As a part of the intelligent transportation system, vehicle detection has become the focus and focus of the research, and has made a lot of widely used results. This paper starts from the direction of vehicle detection. Combined with the domestic and foreign research situation and the domestic traffic flow situation, using the image processing and the machine learning method to carry on the research to the two-wheeled vehicle detection technology, The two-wheel vehicle detection method based on template matching and pre-detection combined with machine learning is adopted. The specific research content is: 1) the research and summary of the domestic and foreign common vehicles and two-wheel vehicle detection technology, and combined with the actual test scene, It is convenient to install traditional vehicle detection technology (such as geomagnetic coil, ultrasonic infrared sensor, etc.) and video-based vehicle detection technology (such as optical flow method, frame difference method, background difference method). The visual analysis and contrast of data processing. (2) using the mean value template of the two wheel vehicle in the foreground to detect the two wheel vehicle. Firstly, the image denoising technology such as median filter and Gao Si filter are used for experimental analysis. The foreground information obtained by frame difference method is processed by morphological expansion several times and the foreground information of mixed Gao Si model is processed and operated in order to obtain a more complete picture. The edge detection method is used to obtain the vehicle edge information in the moving image, and the template is obtained by using the mean value of the two-wheeled vehicle in the foreground, which is fed back to the foreground and matched with the dynamic target in the foreground. Two-wheeled vehicles are detected. The vehicle is counted by the method of centroid trajectory analysis. The hybrid Gao Si model and Ada Boost algorithm are used to detect the vehicle. The detection steps include: using the shape of the car and two-wheeled vehicle. The different characteristics of the character are pre-detected. Using pre-acquired positive and negative samples and Ada Boost machine learning method, the classifier is trained for the LBPHAARHOG feature of the sample, and the classifier is used to detect the two-wheeled vehicle on the pre-detected window. Through the analysis of the training time and the correct detection rate of the classifier in video sequence, the LBP feature, which is the most suitable for the environment of this paper, is obtained. The method of two-wheel vehicle detection based on machine learning proposed in this paper can significantly accelerate the detection speed and effectively reduce the false detection rate.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U495;TP391.41

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本文編號(hào)：1600745