【摘要】：隨著計(jì)算機(jī)技術(shù)的飛速發(fā)展,基于圖像處理的智能交通系統(tǒng)因其實(shí)時(shí)、準(zhǔn)確、高效的特點(diǎn),受到了人們的廣泛關(guān)注。在智能交通系統(tǒng)中,運(yùn)動(dòng)目標(biāo)檢測(cè)與跟蹤可以完成對(duì)交通車輛的智能檢測(cè)與跟蹤、分類識(shí)別等功能。本文重點(diǎn)研究了智能交通中的運(yùn)動(dòng)車輛檢測(cè)與跟蹤算法。為了后期處理方便,提高對(duì)目標(biāo)的識(shí)別跟蹤效果,論文研究了常見(jiàn)的圖像預(yù)處理方法,包括圖像的復(fù)原、灰度化、二值化及形態(tài)學(xué)處理等。在對(duì)運(yùn)動(dòng)車輛檢測(cè)算法進(jìn)行分析研究的基礎(chǔ)上,論文重點(diǎn)仿真了基于LucasKanade模型的光流法、基于Code Book算法的前景檢測(cè)算法以及基于高斯模型的背景相減法等運(yùn)動(dòng)車輛檢測(cè)算法;由于車輛陰影影響到檢測(cè)效果,論文研究了車輛陰影的特點(diǎn)并進(jìn)行了陰影消除;通過(guò)分析相關(guān)算法的優(yōu)缺點(diǎn),提出了結(jié)合基于邊緣三幀差分法與混合高斯背景相減法的運(yùn)動(dòng)車輛提取算法。仿真實(shí)驗(yàn)表明,本算法可以完整的提取運(yùn)動(dòng)車輛區(qū)域,保留運(yùn)動(dòng)車輛的完整信息,提高了算法的實(shí)時(shí)性與魯棒性。在對(duì)運(yùn)動(dòng)車輛跟蹤算法進(jìn)行分類研究后,論文以Kalman濾波和Mean Shift算法為基礎(chǔ),進(jìn)行了車輛跟蹤的算法仿真;在分析了交通車輛的特點(diǎn)后,用色調(diào)分量的概率分布建立特征空間,對(duì)Camshift算法進(jìn)行了仿真研究;結(jié)合實(shí)際交通路況中車輛跟蹤的常見(jiàn)問(wèn)題,諸如車輛的遮擋,背景環(huán)境與目標(biāo)相似,相似車輛毗鄰等情況,仿真了將Camshift算法結(jié)合Kalman濾波的過(guò)程。
[Abstract]:With the rapid development of computer technology, the intelligent transportation system based on image processing has received extensive attention because of its real-time, accurate and efficient characteristics. In intelligent transportation system, moving target detection and tracking can accomplish the functions of intelligent detection and tracking, classification and recognition of traffic vehicles. This paper focuses on the moving vehicle detection and tracking algorithm in intelligent traffic. In order to facilitate the post-processing and improve the target recognition and tracking effect, this paper studies the common image preprocessing methods, including image restoration, grayscale, binarization and morphological processing. Based on the analysis and research of moving vehicle detection algorithm, this paper focuses on simulation of moving vehicle detection algorithms such as optical flow method based on LucasKanade model, foreground detection algorithm based on Code Book algorithm and background subtraction algorithm based on Gao Si model. Because of the influence of vehicle shadow on the detection effect, this paper studies the characteristics of vehicle shadow and eliminates the shadow, and analyzes the advantages and disadvantages of the related algorithms. A moving vehicle extraction algorithm based on edge three frame difference method and hybrid Gao Si background subtraction is proposed. The simulation results show that the proposed algorithm can extract the moving vehicle area completely and retain the complete information of the moving vehicle, and improve the real-time performance and robustness of the algorithm. After studying the classification of moving vehicle tracking algorithm, based on Kalman filter and Mean Shift algorithm, the simulation of vehicle tracking algorithm is carried out, and after analyzing the characteristics of traffic vehicle, the feature space is established by the probability distribution of hue component. The Camshift algorithm is simulated, and the process of combining the Camshift algorithm with Kalman filtering is simulated in combination with the common problems of vehicle tracking in actual traffic conditions, such as vehicle occlusion, similar background environment and target, similar vehicle proximity and so on.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN948.6;U495

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 蔡s，

本文編號(hào)：2182609