本文選題：車型識(shí)別 + 組合特征��； 參考：《浙江大學(xué)》2014年碩士論文

【摘要】：車型識(shí)別作為ITS中的一個(gè)重要分支,在打擊盜竊車輛、規(guī)范交通秩序、高速公路自動(dòng)收費(fèi)等方面具有廣闊的應(yīng)用前景。和其他方法相比,基于監(jiān)控視頻的車型識(shí)別方法不需要在目標(biāo)區(qū)域另外安裝其它感應(yīng)設(shè)備,從而有效的降低了系統(tǒng)的資本投入,并且監(jiān)控視頻所能提供的車型信息更為豐富,故而近些年來(lái)基于監(jiān)控視頻的車型識(shí)別算法取得了諸多研究成果。 本文在研究運(yùn)動(dòng)車輛檢測(cè)、車型特征提取、模式識(shí)別理論的基礎(chǔ)上,設(shè)計(jì)了一套基于車型組合特征和改進(jìn)粒子群參數(shù)優(yōu)化的支持向量機(jī)的車型識(shí)別系統(tǒng),主要成果包括： 1.在利用基于混合高斯背景建模的背景差分法得到運(yùn)動(dòng)車輛的前景圖像之后,結(jié)合YCrCb顏色特征和LBP紋理特征來(lái)進(jìn)行陰影去除： 2.在特征選擇方面,提出了利用車輛圖像的組合特征,包括Hu矩特征、LBPS特征、長(zhǎng)寬比特征來(lái)表述車輛的特征信息,可以有效的解決單一特征容易受到光照、天氣、噪聲等影響,以及在識(shí)別中精度有限的問(wèn)題； 3.提出了一種新的LBPS特征,在LBP特征的基礎(chǔ)上做了改進(jìn),采用分塊和信息熵的思想在車輛的紋理信息中加入了空間信息,同時(shí)降低了紋理特征的維度； 4.提出了一種改進(jìn)的粒子群算法來(lái)進(jìn)行支持向量機(jī)的參數(shù)尋優(yōu)。在速度更新公式中增加對(duì)任一隨機(jī)粒子的跟隨,之后引入動(dòng)量項(xiàng)的概念,同時(shí)考慮前一個(gè)時(shí)刻和前前時(shí)刻的速度變化情況,可以有效的解決基本粒子群算法容易陷入局部極值點(diǎn)和后期震蕩嚴(yán)重收斂放緩的問(wèn)題。
[Abstract]:As an important branch of ITS, vehicle recognition has a broad application prospect in cracking down on theft of vehicles, standardizing traffic order and automatic toll collection on highways.Compared with other methods, the method of vehicle identification based on surveillance video does not need to install other sensing equipment in the target area, thus effectively reducing the capital investment of the system, and the monitoring video can provide more information about the vehicle type.Therefore, in recent years, vehicle recognition algorithm based on surveillance video has made a lot of research results.Based on the research of moving vehicle detection, vehicle feature extraction and pattern recognition theory, a vehicle recognition system based on vehicle combination feature and improved particle swarm optimization is designed in this paper. The main results are as follows:1.After the background difference method based on mixed Gao Si background modeling is used to get the foreground image of moving vehicle, the shadow is removed by combining YCrCb color feature and LBP texture feature.2.In the aspect of feature selection, it is put forward that the combination features of vehicle image, including Hu moment feature, LBPS feature and aspect ratio feature, can be used to express the feature information of vehicle, which can effectively solve the problem that single feature is easily affected by illumination, weather, noise and so on.And the problem of limited precision in recognition;3.A new LBPS feature is proposed, which is improved on the basis of LBP feature. The idea of partition and information entropy is used to add spatial information to the texture information of vehicle, and the dimension of texture feature is reduced.4.An improved particle swarm optimization algorithm is proposed for parameter optimization of support vector machines.The following of any random particle is added to the velocity update formula, then the concept of momentum term is introduced, and the velocity variation of the previous moment and the preceding moment is considered.It can effectively solve the problem that the basic particle swarm optimization algorithm is prone to slow down the convergence of local extremum and late oscillation.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TN948.6

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本文編號(hào)：1745458