本文選題：車輛識別　切入點(diǎn)：視覺檢測　出處：《南京理工大學(xué)》2016年碩士論文

【摘要】：本文研究了基于單目視覺的行車預(yù)警系統(tǒng),分別在白天車輛檢測、夜間車輛檢測以及車輛跟蹤三大技術(shù)領(lǐng)域內(nèi)對國內(nèi)外各種研究成果進(jìn)行了分析對比,根據(jù)不同算法的特點(diǎn)以及在實(shí)際場景中遇到的問題設(shè)計(jì)了相應(yīng)的改進(jìn)算法。在白天場景下的車輛檢測中,針對傳統(tǒng)的基于Hough變換的車道線檢測算法不能提取彎道車道線的缺點(diǎn),設(shè)計(jì)了基于對稱搜索的四次多項(xiàng)式車道線擬合算法,減小了擬合誤差。針對直接將疑似車輛目標(biāo)結(jié)果輸入到SVM分類器進(jìn)行分類的方法效率低的特點(diǎn)設(shè)計(jì)了基于PCA降維算法的驗(yàn)證算法,即通過提取初步檢測結(jié)果的特征向量簡化分類器的輸入提高算法的檢測效率。在夜間車輛檢測中,針對傳統(tǒng)算法通過單一顏色閾值來分割車輛尾燈會誤檢測出其他發(fā)光背景的情況,分別建立了尾燈紅色光暈以及白色中心的HSV顏色模型,通過分別提取這兩種顏色來識別出車輛尾燈,并在獲得車輛尾燈初步檢測的基礎(chǔ)上利用證據(jù)融合算法來驗(yàn)證檢測到的車輛尾燈。在車輛跟蹤過程中,設(shè)計(jì)了基于運(yùn)動模型和跟蹤隊(duì)列的車輛跟蹤算法,通過運(yùn)動模型預(yù)估車輛目標(biāo)在下一幀中的位置,利用圖像匹配算法在預(yù)估位置周圍搜索車輛目標(biāo),用搜索結(jié)果對運(yùn)動模型進(jìn)行修正,并通過建立目標(biāo)的跟蹤隊(duì)列來完成對車輛的跟蹤。通過對實(shí)際道路場景上拍攝的視頻進(jìn)行處理的結(jié)果表明,在Windows 10操作系統(tǒng)以及3.2GHz CPU的環(huán)境下,采用本文設(shè)計(jì)的車輛檢測與跟蹤方法能夠準(zhǔn)確的檢測出前方車輛并且能夠達(dá)到每秒20幀的檢測速度,能夠滿足實(shí)時(shí)性的要求。
[Abstract]:In this paper, the vehicle warning system based on monocular vision is studied. The research results are analyzed and compared in three technical fields: daytime vehicle detection, night vehicle detection and vehicle tracking. According to the characteristics of different algorithms and the problems encountered in the actual scene, the corresponding improved algorithm is designed. In the daytime vehicle detection, the traditional lane line detection algorithm based on Hough transform can not extract the curve lane line. In this paper, a new algorithm for lane fitting based on symmetric search is proposed. The fitting error is reduced. A verification algorithm based on PCA dimension reduction algorithm is designed for the low efficiency of the method of directly inputting the suspected vehicle target results into the SVM classifier for classification. That is, the detection efficiency of the algorithm is improved by extracting the eigenvector of the preliminary detection results, which simplifies the input of the classifier. Aiming at the situation that the traditional algorithm can detect the other luminous background by using a single color threshold, the HSV color model of the red halo and the white center of the taillight is established. The two colors are extracted to identify the vehicle taillights, and the evidence fusion algorithm is used to verify the detected taillights on the basis of obtaining the initial detection of the vehicle taillights. The vehicle tracking algorithm based on motion model and tracking queue is designed. The motion model is used to estimate the position of vehicle target in the next frame, and the image matching algorithm is used to search the vehicle target around the predicted position. The motion model is modified with the search results, and the vehicle tracking is accomplished by setting up the tracking queue of the target. The results of processing the video taken on the actual road scene show that, In the environment of Windows 10 operating system and 3.2GHz CPU, the vehicle detection and tracking method designed in this paper can accurately detect forward vehicles and achieve 20 frames per second detection speed, which can meet the real-time requirements.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U463.6;TP277

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