本文選題：智能視頻監(jiān)控 + 人流量統(tǒng)計(jì)　； 參考：《合肥工業(yè)大學(xué)》2016年碩士論文

【摘要】：人流量統(tǒng)計(jì)問題是當(dāng)前智能監(jiān)控領(lǐng)域的前沿性課題,涉及到諸多學(xué)科和眾多復(fù)雜的問題。在智能視頻監(jiān)控下對人流量進(jìn)行統(tǒng)計(jì)不僅能應(yīng)用在商場超市等商業(yè)領(lǐng)域,還能在安防、交通等方面發(fā)揮重要作用�；谥悄芤曨l監(jiān)控的人流量統(tǒng)計(jì)系統(tǒng)是利用智能視頻監(jiān)控技術(shù)對監(jiān)控區(qū)域的人流量進(jìn)行統(tǒng)計(jì),其核心技術(shù)包括運(yùn)動(dòng)目標(biāo)檢測、人體目標(biāo)識(shí)別和跟蹤。本文針對開放環(huán)境下的人流量統(tǒng)計(jì)需求,設(shè)計(jì)了基于智能視頻監(jiān)控的人流量統(tǒng)計(jì)系統(tǒng),并對其中的關(guān)鍵技術(shù)進(jìn)行了分析和研究,主要的工作如下：(1)本文在分析了多種運(yùn)動(dòng)目標(biāo)檢測算法后,通過對當(dāng)前主流背景建模算法的評估和比較,確定了基于ViBe的運(yùn)動(dòng)目標(biāo)檢測算法。針對傳統(tǒng)ViBe算法的不足之處,提出了一種改進(jìn)的算法,通過對前景像素點(diǎn)做鬼影判定進(jìn)行二次分類,有效地消除了鬼影并抑制了閃爍點(diǎn)像素噪聲。(2)在人體目標(biāo)識(shí)別和跟蹤方面,以HOG特征作為行人描述特征,結(jié)合改進(jìn)的ViBe算法得到可能運(yùn)動(dòng)區(qū)域的HOG特征值,然后通過事先訓(xùn)練好的SVM分類器進(jìn)行分類識(shí)別。本文采用Cam-shift跟蹤算法實(shí)現(xiàn)對人體目標(biāo)的跟蹤,針對目標(biāo)被遮擋時(shí)出現(xiàn)的無法匹配的現(xiàn)象,結(jié)合Kalman濾波器對目標(biāo)運(yùn)動(dòng)軌跡進(jìn)行預(yù)測。(3)基于上述關(guān)鍵技術(shù)的研究,本文最終完成了系統(tǒng)軟、硬件平臺(tái)的設(shè)計(jì),并開發(fā)出客戶端界面。系統(tǒng)經(jīng)過調(diào)試和實(shí)驗(yàn),實(shí)現(xiàn)了對人流量的準(zhǔn)確統(tǒng)計(jì),具有較好的魯棒性。
[Abstract]:The problem of human flow statistics is a leading subject in the field of intelligent monitoring, which involves many disciplines and complex problems. Under intelligent video surveillance, the statistics of human flow can not only be used in commercial fields such as shopping malls and supermarkets, but also play an important role in security, traffic and so on. The human flow statistics system based on intelligent video surveillance is to use intelligent video surveillance technology to calculate the flow of people in the monitored area. The core technologies of the system include moving target detection, human body target recognition and tracking. In order to meet the demand of human flow statistics in open environment, this paper designs a human flow statistics system based on intelligent video surveillance, and analyzes and studies the key technologies. The main work is as follows: (1) after analyzing a variety of moving target detection algorithms, through the evaluation and comparison of the current mainstream background modeling algorithms, we determine the moving target detection algorithm based on ViBe. In view of the shortcomings of the traditional ViBe algorithm, an improved algorithm is proposed, which can effectively eliminate the ghost shadow and suppress the pixel noise of the flashing point in the aspect of human body target recognition and tracking by making a second classification of the foreground pixels. The HOG feature is used as the pedestrian description feature, and the HOG eigenvalue of the possible moving region is obtained by combining the improved ViBe algorithm, and then it is recognized by the pre-trained SVM classifier. In this paper, Cam-shift tracking algorithm is used to track human body target. Aiming at the unmatched phenomenon when the target is occluded, combined with the Kalman filter to predict the moving trajectory of the target, the research is based on the above key technology. Finally, the software and hardware platform of the system is designed, and the client interface is developed. After debugging and experiment, the system realizes the accurate statistics of the flow of people, and has good robustness.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN948.6
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本文編號(hào)：1989020