【摘要】：當(dāng)今社會的發(fā)展對于智能交通系統(tǒng)的管理和服務(wù)功能需求越來越高,國內(nèi)外對此方面的研究均相當(dāng)重視。而車輛檢測技術(shù)是智能交通系統(tǒng)的根基。在對傳統(tǒng)的目標(biāo)檢測技術(shù)和相關(guān)模型系統(tǒng)進(jìn)行分析總結(jié)的基礎(chǔ)上,本文提出了一種將幀差法和AdaBoost相結(jié)合的車輛檢測技術(shù)。主要包含了以下幾方面的工作： 一.與傳統(tǒng)幀差法相異,本文幀差法的主要目的是將原幀圖像中的車輛灰度特征進(jìn)行簡化顯示,沒有采取二值化操作,而是將帶有豐富灰度信息的幀差圖像進(jìn)入下面步驟的處理。 二.通過圖像的濾波處理進(jìn)一步去除圖像中的顆粒型噪聲,將運動信息從其中進(jìn)一步突出出來。 三.采用AdaBoost的方法,以幀差圖像中提取的車輛和非車輛樣本作為訓(xùn)練樣本訓(xùn)練弱分類器,并通過一定步驟的迭代操作后得出最終的強分類器。由于有前面的處理方法突出了運動目標(biāo),因此需要進(jìn)行的學(xué)習(xí)大大簡化。 最后經(jīng)過實驗,本文中的識別系統(tǒng)對于道路車輛的識別取得了較好的識別效果和可以接受的識別速度。
[Abstract]:With the development of the society, more and more attention is paid to the management and service function of intelligent transportation system. Vehicle detection technology is the foundation of intelligent transportation system. Based on the analysis and summary of traditional target detection technology and related model system, this paper proposes a vehicle detection technology which combines frame difference method and AdaBoost. Mainly includes the following aspects of work: 1. Different from the traditional frame difference method, the main purpose of the frame difference method is to simplify the display of the vehicle gray level features in the original frame image, instead of taking a binary operation, but to process the frame difference image with rich gray level information into the following steps. II. The granular noise in the image is further removed by filtering the image, and the motion information is further highlighted from it. Three The method of AdaBoost is used to train the weak classifier with the vehicle and non-vehicle samples extracted from the frame difference image as the training samples, and the final strong classifier is obtained after a certain iterative operation. The learning needs to be greatly simplified because the previous processing method highlights the moving target. Finally, through the experiment, the recognition system in this paper has achieved better recognition effect and acceptable recognition speed for road vehicle recognition.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.41;U495

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本文編號：2416617