本文選題：車輛檢測 + 稀疏表示　； 參考：《江蘇科技大學(xué)》2016年碩士論文

【摘要】：基于視頻的車輛檢測是智能交通系統(tǒng)的基礎(chǔ)和關(guān)鍵,也是計算機視覺領(lǐng)域的研究熱點。隨著研究的深入,近年來提出了許多新的車輛檢測算法,逐步解決了檢測過程中出現(xiàn)車輛陰影與背景擾動的問題,然而這些算法仍然存在著一些缺點和不足,如沒有考慮雨雪霧霾等惡劣天氣環(huán)境下視頻圖像的噪聲和干擾問題,以及車輛檢測算法的復(fù)雜度高所帶來的實時性問題等。因此,本文針對當(dāng)前基于視頻的車輛檢測算法中存在的噪聲干擾和實時性問題,提出了具體的提高檢測率和實時性的方法。主要的研究工作有以下兩個方面:(1)針對視頻中噪聲影響車輛檢測率的問題,深入研究了中值濾波、維納濾波和小波濾波這三種常用的去噪算法,分析它們在實際應(yīng)用中的不足,提出了一種基于稀疏表示的視頻去噪方法。利用K-奇異值分解(K-Singular Value Decomposition,K-SVD)算法訓(xùn)練過完備字典,將噪聲圖像在過完備字典上稀疏分解,根據(jù)圖像信號能夠在過完備字典上稀疏分解而噪聲不能稀疏分解的原理,去除噪聲,恢復(fù)圖像。與上述三種去噪方法相比,本文提出的去噪方法能夠明顯提高惡劣天氣或環(huán)境下視頻圖像的信噪比,改善圖像質(zhì)量,有助于提高車輛的檢測率。(2)圍繞背景差分方法中如何建立高魯棒性的背景模型問題,在深入分析和研究均值法、單高斯背景模型和混合高斯背景模型三種經(jīng)典的建模算法的基礎(chǔ)上,提出了一種基于混合高斯的多模態(tài)模型的優(yōu)化方案。由于建模過程中所得到的所有模型都有可能是背景模型,模型權(quán)值較小的像素中有可能也包含了真實背景像素。因此,舍棄建模過程中模型匹配后的權(quán)值排序、累加值與閾值比較這兩個計算步驟,從而達(dá)到既能建立更真實的背景模型又能降低算法的計算量的效果。最后,利用質(zhì)心跟蹤法完成多車輛的跟蹤,并統(tǒng)計車流量。實驗結(jié)果表明,本文提出的基于混合高斯的多模態(tài)建模的車輛檢測算法具有抗干擾能力強、計算量低和檢測效果好的優(yōu)點。
[Abstract]:Vehicle detection based on video is the basis and key of Intelligent Transportation system (its), and it is also a hotspot in the field of computer vision. With the deepening of research, many new vehicle detection algorithms have been proposed in recent years, which gradually solve the problem of vehicle shadow and background disturbance in the detection process. However, these algorithms still have some shortcomings and shortcomings. Such as not considering the noise and interference of video image in severe weather such as rain, snow and haze, and the real-time problem caused by the high complexity of vehicle detection algorithm, etc. Therefore, aiming at the noise interference and real-time problems in the current video based vehicle detection algorithm, this paper proposes a specific method to improve the detection rate and real-time performance. The main research work has the following two aspects: 1) aiming at the problem that the noise in the video affects the vehicle detection rate, three commonly used de-noising algorithms, median filter, Wiener filter and wavelet filter, are studied in depth, and their shortcomings in practical application are analyzed. A method of video denoising based on sparse representation is proposed. Using K-Singular value decomposition (K-Singular value DecompositionK-SVD) algorithm to train over-complete dictionaries, the noise images are sparse decomposed in over-complete dictionaries. According to the principle that image signals can be sparse decomposed in over-complete dictionaries but noise cannot be sparse decomposed, the noise is removed. Restore the image. Compared with the above three denoising methods, the proposed denoising method can significantly improve the SNR and image quality of video images in severe weather or environment. It is helpful to improve the detection rate of vehicles. (2) focusing on how to establish a background model with high robustness in background difference method, this paper analyzes and studies the mean value method in depth. On the basis of three classical modeling algorithms of single Gao Si background model and mixed Gao Si background model, an optimization scheme of multimodal model based on hybrid Gao Si is proposed. Since all the models obtained in the modeling process are likely to be background models, it is possible that the pixels with small weights of the model may also contain real background pixels. Therefore, the weight ranking after model matching is abandoned, and the cumulative value is compared with the threshold in order to establish a more realistic background model and reduce the computational cost of the algorithm. Finally, the centroid tracking method is used to track multiple vehicles, and the traffic flow is counted. The experimental results show that the vehicle detection algorithm proposed in this paper based on hybrid Gao Si has the advantages of strong anti-jamming ability, low computational complexity and good detection effect.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U495;TP391.41

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