本文選題：車輛檢測(cè) + 隱藏變量部件模型　； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：汽車保有量的逐年增加,攝像頭的大量應(yīng)用,使得交通場(chǎng)景中車輛的自動(dòng)化管理已經(jīng)成為一大難題。交通場(chǎng)景圖像中車輛檢測(cè)和分類技術(shù)是解決這一問(wèn)題的重要手段,論文選題具有重要的理論意義和實(shí)際應(yīng)用價(jià)值。論文主要工作如下:1.給出了一種針對(duì)車輛的隱藏變量部件模型訓(xùn)練方法�；陔[藏變量支持向量機(jī),對(duì)每類車型都分別訓(xùn)練了隱藏變量部件模型用于車輛檢測(cè),模型包含三個(gè)部分:主模型、部件模型及部件空間位置關(guān)系。車輛模型不僅可以從整體上描述車輛的外觀輪廓信息,還可以從細(xì)節(jié)上描述車輛的部件輪廓信息。實(shí)驗(yàn)表明,訓(xùn)練得到的各類車輛模型可以有效的在交通場(chǎng)景圖像中檢測(cè)出車輛的位置。2.給出了一種基于隱藏變量部件模型的車輛分類方法。用訓(xùn)練得到的各類車輛模型分別檢測(cè)交通場(chǎng)景圖像,選擇響應(yīng)值最大模型的檢測(cè)結(jié)果提取車輛圖像區(qū)域。在提取的車輛圖像區(qū)域中用所有類別模型進(jìn)行模型配準(zhǔn),找到最佳的、可以代表各類車型特征的主模型及部件模型位置,能夠最大程度的反應(yīng)車輛的獨(dú)有信息,具有較大的區(qū)分度。提取所有位置的HOG特征作為圖像的表示,利用SVM分類器進(jìn)行分類。經(jīng)實(shí)驗(yàn)表明,同當(dāng)前已有方法對(duì)比,本文所提方法具有更高的分類準(zhǔn)確率。3.給出了一種基于卷積神經(jīng)網(wǎng)絡(luò)的車輛分類方法。使用卷積神經(jīng)網(wǎng)絡(luò)(CNN)對(duì)模型配準(zhǔn)得到的主模型及部件模型位置進(jìn)行深度特征提取,將得到的高維深度特征進(jìn)行主成分分析(PCA),再利用SVM分類器進(jìn)行分類。實(shí)驗(yàn)結(jié)果表明,該方法可以有效的提升分類準(zhǔn)確率。
[Abstract]:With the increase of vehicle ownership and the application of cameras, the automatic management of vehicles in traffic scene has become a big problem. Vehicle detection and classification technology in traffic scene images is an important means to solve this problem. The topic of this paper has important theoretical significance and practical application value. The main work of this paper is as follows: 1. A training method of hidden variable component model for vehicle is presented. Based on the hidden variable support vector machine (SVM), the hidden variable component model is trained for each type of vehicle for vehicle detection. The model consists of three parts: the main model, the component model and the spatial position relationship of the components. The vehicle model can not only describe the contour information of the vehicle as a whole, but also describe the contour information of the parts of the vehicle in detail. Experiments show that all kinds of vehicle models can effectively detect the position of vehicles in traffic scene images. A vehicle classification method based on hidden variable component model is presented. The traffic scene images are detected by training vehicle models, and the vehicle image regions are extracted by selecting the detection results of the maximum response model. In the extracted vehicle image region, all kinds of models are used for model registration to find the best location of the main model and the component model, which can represent the characteristics of various types of vehicle, and can reflect the unique information of the vehicle to the greatest extent. It has a large degree of differentiation. The HOG feature of all positions is extracted as the representation of the image, and the SVM classifier is used to classify the image. The experimental results show that the proposed method has a higher classification accuracy. 3. A vehicle classification method based on convolution neural network is presented. By using convolution neural network (CNN), the location of the main model and the component model was extracted. The high dimensional depth features were analyzed by principal component analysis (PCA) and then classified by SVM classifier. Experimental results show that this method can effectively improve the classification accuracy.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495;TP391.41

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 姜尚潔;羅斌;劉軍;張?jiān)?;基于無(wú)人機(jī)的車輛目標(biāo)實(shí)時(shí)檢測(cè)[J];測(cè)繪通報(bào);2017年S1期

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本文編號(hào)：1825834