本文選題：兩輪車輛　切入點：前景提取　出處：《江西理工大學(xué)》2014年碩士論文　論文類型：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,對于交通系統(tǒng)的安全、便捷的運行不言而喻。作為智能交通系統(tǒng)一部分的車輛檢測也因此成為了研究的熱點和重點,并取得了很多廣泛應(yīng)用的成果。本文從車輛檢測的方向出發(fā),結(jié)合國內(nèi)外的研究現(xiàn)狀以及國內(nèi)的交通流狀況,使用圖像處理和機器學(xué)習(xí)的方法對兩輪車輛的檢測技術(shù)進行研究,采用基于模板匹配和預(yù)檢測結(jié)合機器學(xué)習(xí)的方法進行兩輪車輛檢測,具體的研究內(nèi)容是:(1)研究和總結(jié)了國內(nèi)外對普通車輛以及兩輪車輛檢測技術(shù),并結(jié)合實際的試驗場景,對傳統(tǒng)的車輛檢測技術(shù)(如地磁線圈、超聲紅外傳感等)和基于視頻的車輛檢測技術(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)處理的直觀性進行分析和對比。(2)采用前景中兩輪車輛的均值模板對兩輪車輛進行檢測。首先對常采用圖像消噪技術(shù)如中值濾波、高斯濾波進行實驗分析說明,通過對幀差法獲得的前景信息進行多次形態(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。使用邊緣檢測的方法獲得運動圖像中的車輛邊緣信息,并利用前景中兩輪車輛的均值獲得模板,并反饋到前景中,與前景中的動態(tài)目標(biāo)進行模板匹配,檢測兩輪車輛。利用車輛質(zhì)心軌跡分析的方法對檢測的車輛進行計數(shù)。(3)運用混合高斯模型和Ada Boost算法進行車輛檢測。檢測步驟包括:利用小汽車和兩輪車輛的形狀特征的不同性進行預(yù)檢測,使用預(yù)先獲取的正樣本和負(fù)樣本以及Ada Boost機器學(xué)習(xí)的方法對樣本的LBP、HAAR、HOG特征分別進行分類器的訓(xùn)練。并使用分類器在預(yù)檢測的窗口上進行兩輪車輛的檢測,通過訓(xùn)練時間以及訓(xùn)練得到的分類器在視頻序列中的檢測的正確率的分析,得出最符合本文環(huán)境的檢測特征,即LBP特征。實驗表明,本文提出的在預(yù)檢測的基礎(chǔ)上使用機器學(xué)習(xí)進行兩輪車輛檢測的方法可以明顯加快檢測速度,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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;TP391.41

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