本文選題：人群密度估計　切入點：非參數(shù)背景模型　出處：《電子科技大學》2011年碩士論文

【摘要】：隨著城市化建設進程的加快以及經(jīng)濟社會的高速發(fā)展,如娛樂活動、展覽活動、體育賽事、慶祝活動等這些大規(guī)模的人群密集活動將會愈加頻繁出現(xiàn),這些公共活動的安全問題成為相關部門關注的焦點,同時在計算機視覺和數(shù)字圖像處理領域安全監(jiān)控中人群密度估計方法也成為研究的熱點。 本文研究的重點在于室外實時監(jiān)控中人群密度估計方法。通過分析國內(nèi)外對人群密度估計研究現(xiàn)狀和所取得的進展,采用基于像素特征和紋理特征相結(jié)合的方式分別對低密度人群和高密度人群進行人群密度估計。通過自適應背景建模得到背景模型然后利用圖像分割得到人群前景,在對前景人群進行陰影抑制和形態(tài)學處理的去噪操作后計算前景面積,若其占整個圖像面積比例較大時將其判定為高密度人群圖像,反之視為低密度圖像。 在低密度情況下,對提取出的人群前景進行輪廓檢測并計算輪廓像素數(shù)目,最后根據(jù)多個低密度人群圖像樣本的計算結(jié)果進行最小二乘直線擬合得到人群數(shù)目關于前景輪廓像素數(shù)目的直線方程,此后就可以利用該直線方程通過計算人群前景輪廓像素數(shù)目大概估計出人群數(shù)目。 在高密度情況下,將高密度人群分為高、偏高和極高三個密度等級,利用灰度共生矩陣進行紋理分析,提取常用的紋理特征,然后通過主成分分析法確定最重要的4個特征作為高密度人群圖像的紋理特征,然后采用支持向量機進行訓練分類。 本文采用了一種基于影響因素描述的非參數(shù)背景模型,實驗證明用其在室外復雜場景中建模得到的背景圖像十分清晰,具備較強的魯棒性;此外,將人群前景二值化操作融入前景提取過程,輪廓檢測采用形態(tài)學處理的思想以及特征提取利用主成分分析法等對實時性有極大的提高。
[Abstract]:With the rapid development of city construction and to accelerate the process of economic and social activities, such as entertainment, exhibitions, sports events, celebrations etc. these large-scale crowd activities will be more frequent, security issues of these public activities become the focus of the related departments, both in computer vision and digital image processing method has become a research the hot field of safety monitoring of crowd density estimation.
This paper focuses on the crowd density estimation method of outdoor real-time monitoring. Through the analysis of domestic and foreign research status and the progress of crowd density estimation, using pixel features and texture features based on the combination of low density and high density population population into the crowd density estimation. Through adaptive background modeling and background model the use of image segmentation to get people in prospect, prospects for the crowd to suppress shadow and morphological processing denoising operation after calculating foreground area, if it occupies the entire image area when it is judged as a larger proportion of high density population and low density images, as images.
In the low density case, contour detection of foreground extracted crowd and calculate the contour pixel number, according to the calculation of multiple low density population image sample results are get linear equation groups regarding the number of foreground contour pixels the least squares fitting, then it can be calculated through the crowd foreground contour pixel number roughly estimate the number of people by using the linear equation.
In the condition of high density, high density will be divided into groups of high, high and very high density level, using gray level co-occurrence matrix for texture analysis, texture feature extraction commonly used, and then determine the 4 most important features of the image texture features as high density population through principal component analysis, and then the classification of training support vector machine.
This paper uses a nonparametric background model is described based on the influencing factors, the experiment shows that the background image obtained in the outdoor complex scene modeling is very clear, with strong robustness; in addition, the crowd binarization operation into the foreground extraction process, contour detection using morphological processing and feature extraction using the principal thought component analysis is greatly improved in real time.

【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TP391.41

【引證文獻】

相關碩士學位論文 前2條

1 劉文昭;基于圖像識別的電梯群控系統(tǒng)研究[D];電子科技大學;2012年

2 姬媛媛;中學生步行速度突變對擠踏形成和疏散的影響[D];首都經(jīng)濟貿(mào)易大學;2012年

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本文編號：1727808