本文關(guān)鍵詞：道路人流量狀態(tài)監(jiān)測　出處：《貴州民族大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 光流檢測 粒子動(dòng)力學(xué) 運(yùn)動(dòng)流分割 多元線性回歸 流量狀態(tài)監(jiān)測

【摘要】：隨著社會(huì)發(fā)展和城鎮(zhèn)化加速推進(jìn),群體性社會(huì)活動(dòng)逐漸增多,例如運(yùn)動(dòng)會(huì)、音樂會(huì)、宗教集會(huì)等。在類似的場景中,由于人流之間運(yùn)動(dòng)快慢和方向不一,局部區(qū)域的人群密度不同,加上建筑通道承載量的客觀限制,使得在群體性活動(dòng)中極易發(fā)生人群擁堵,甚至踩踏事故,造成生命財(cái)產(chǎn)損失。為有效地避免群體性活動(dòng)中意外事故的發(fā)生,智能監(jiān)控技術(shù)已經(jīng)被廣泛地應(yīng)用于車站、機(jī)場、地鐵以及大型的購物中心等領(lǐng)域。而在群體性智能監(jiān)控中,人群密度估計(jì)是后續(xù)狀態(tài)監(jiān)測的核心技術(shù),也是計(jì)算機(jī)視覺和圖像處理領(lǐng)域的研究熱點(diǎn)。為了對群體進(jìn)行有效的人流量狀態(tài)監(jiān)測,提高道路的利用率,避免公民不必要的生命財(cái)產(chǎn)損失,提出了一種道路人流量狀態(tài)監(jiān)測方法,主要包括運(yùn)動(dòng)信息提取、人群前景目標(biāo)檢測、運(yùn)動(dòng)流分割和人流量狀態(tài)識別。開展的具體工作如下:(1)采用全局光流法獲取場景的運(yùn)動(dòng)信息,包括運(yùn)動(dòng)方向和快慢;(2)借鑒粒子動(dòng)力學(xué)的理論方法實(shí)現(xiàn)前景目標(biāo)檢測;(3)以運(yùn)動(dòng)前景為掩模提取對應(yīng)幀的光流角度,再利用動(dòng)態(tài)C均值聚類方法實(shí)現(xiàn)前景運(yùn)動(dòng)流分割;(4)在光流場中計(jì)算不同運(yùn)動(dòng)流的前景光流面積特征和基于光流角度的能量、熵、均勻度、對比度等紋理特征,并以此作為樣本的特征向量,建立多元線性回歸模型,實(shí)現(xiàn)人群密度估計(jì),進(jìn)而達(dá)到對人流量狀態(tài)監(jiān)測的目的。(5)將建議算法與自組織神經(jīng)網(wǎng)絡(luò)方法進(jìn)行測試比較,對比驗(yàn)證本文算法的優(yōu)越性。通過對比實(shí)驗(yàn)證實(shí),本文算法切實(shí)可行,為后續(xù)行為分析和理解提供了重要的參考價(jià)值。
[Abstract]:With the social development and the accelerating urbanization, group social activities gradually increased, such as games, concerts, and other religious gatherings. In a similar scenario, due to the flow of people between the speed of movement and direction of a different population density in local area, building channels plus bearing objective restrictions, so prone to the crowd congestion in group activities, and even a stampede, resulting in the loss of life and property. In order to effectively avoid accidents occurred in group activities, intelligent monitoring technology has been widely used in the station, airport, subway, shopping malls and other fields. In the group of intelligent monitoring, crowd density estimation is the core technology of state monitoring of follow-up, it is also a research hotspot in computer vision and image processing. In order to carry out effective monitoring the flow of people in groups, improve the utilization of the road to avoid Citizens of the unnecessary loss of life and property, proposed a road traffic monitoring method, including motion information extraction, the crowd foreground object detection, motion segmentation and traffic state recognition. The specific work is as follows: (1) the global optical flow method to obtain the motion information of the scene, including the movement direction and speed; (2) from the theory of particle dynamics for foreground object detection; (3) to the foreground mask for the extraction of the corresponding frame optical flow angle, using dynamic C clustering method to realize the moving foreground segmentation; (4) the prospect of optical flow calculation of different flow in the area features in the optical flow field and optical flow of energy, based on entropy, uniformity, contrast and texture features, and as a feature vector of sample, establish multiple linear regression model, the estimated density of the crowd, so as to the traffic condition monitoring purposes. (5) comparing the proposed algorithm with the self-organizing neural network, the superiority of the algorithm is verified by comparison. Compared with experiments, the algorithm is feasible and provides an important reference value for subsequent behavior analysis and understanding.

【學(xué)位授予單位】：貴州民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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