本文選題：人群統(tǒng)計(jì) + 頭肩輪廓特征�。� 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：目前,智能視頻監(jiān)控領(lǐng)域飛速發(fā)展,視頻監(jiān)控應(yīng)用到日常生活中的方方面面。智能視頻監(jiān)控就是使用計(jì)算機(jī)視覺(jué)和圖像處理的相關(guān)處理方法,將圖像中的待檢測(cè)目標(biāo)檢測(cè)出來(lái),對(duì)圖像中待檢測(cè)目標(biāo)的行為特征進(jìn)行理解,而且行人目標(biāo)的檢測(cè)及跟蹤是目前圖像處理研究中的一大熱點(diǎn)。本文研究的是“智能監(jiān)控關(guān)鍵技術(shù)及其應(yīng)用研究”中的子問(wèn)題——“通道場(chǎng)景下的人群統(tǒng)計(jì)系統(tǒng)”,實(shí)現(xiàn)對(duì)道場(chǎng)景下的行人進(jìn)行檢測(cè)和流量統(tǒng)計(jì)。本文通過(guò)對(duì)基于視頻監(jiān)控的行人檢測(cè)技術(shù)進(jìn)行研究分析,在已有研究成果的基礎(chǔ)之上,實(shí)現(xiàn)了通道場(chǎng)景下人群統(tǒng)計(jì)系統(tǒng)的系統(tǒng)原型,論文的主要工作包括:1、人體運(yùn)動(dòng)目標(biāo)檢測(cè)算法,綜合快速三幀差分和頭肩輪廓特征對(duì)人體運(yùn)動(dòng)目標(biāo)進(jìn)行快速檢測(cè),通過(guò)人體對(duì)頭肩輪廓模型的檢測(cè),實(shí)現(xiàn)人體目標(biāo)的快速檢測(cè),以滿(mǎn)足實(shí)時(shí)性要求。2、頭肩輪廓模型檢測(cè)方法,首先提取頭肩輪廓特征,將提取出來(lái)的頭肩輪廓與樣本庫(kù)中的頭肩輪廓進(jìn)行匹配,若匹配成功,則判斷該目標(biāo)為人體目標(biāo),否則為非人體目標(biāo)。3、人體運(yùn)動(dòng)目標(biāo)跟蹤算法,綜合利用人體運(yùn)動(dòng)目標(biāo)檢測(cè)階段得到人體頭肩輪廓,用對(duì)人體頭肩的跟蹤來(lái)代替對(duì)人體運(yùn)動(dòng)目標(biāo)的跟蹤。采用人體頭肩輪廓特征,結(jié)合卡爾曼濾波,通過(guò)將人體頭肩輪廓與人體頭肩樣本庫(kù)中的樣本進(jìn)行匹配,實(shí)現(xiàn)對(duì)人體頭肩的跟蹤,采用人體頭肩的最小外接矩形來(lái)代替整個(gè)人體運(yùn)動(dòng)目標(biāo)。4、多個(gè)人體運(yùn)動(dòng)目標(biāo)跟蹤算法,該算法對(duì)多個(gè)人體運(yùn)動(dòng)目標(biāo)在運(yùn)動(dòng)過(guò)程中產(chǎn)生的遮擋、重疊、分離情況進(jìn)行了處理,能夠?qū)崿F(xiàn)對(duì)多個(gè)人體運(yùn)動(dòng)目標(biāo)的跟蹤檢測(cè)。5、人群流量統(tǒng)計(jì)算法,該算法通過(guò)設(shè)置擴(kuò)展雙向計(jì)數(shù)線(xiàn),對(duì)通過(guò)計(jì)數(shù)線(xiàn)的行人目標(biāo)進(jìn)行計(jì)數(shù),實(shí)現(xiàn)對(duì)人體目標(biāo)的流量統(tǒng)計(jì)。本原型系統(tǒng)是在Windows操作系統(tǒng)下的VS2008平臺(tái)上,運(yùn)用OpenCV庫(kù)進(jìn)行開(kāi)發(fā)的,系統(tǒng)開(kāi)發(fā)語(yǔ)言是C++,硬件包含一個(gè)攝像頭,一臺(tái)筆記本電腦,其中CPU主頻2.0GHZ,內(nèi)存2G。
[Abstract]:At present, the field of intelligent video surveillance is developing rapidly, and video surveillance is applied to every aspect of daily life. Intelligent video surveillance is to use computer vision and image processing related processing methods to detect the target to be detected in the image and to understand the behavior characteristics of the target to be detected in the image. And pedestrian target detection and tracking is a hot spot in image processing. In this paper, the key technology of intelligent monitoring and its application is studied, which is called "crowd Statistics system in Channel scene", which realizes the detection and flow statistics of pedestrians in the road scene. Based on the research and analysis of pedestrian detection technology based on video surveillance, this paper realizes the prototype of the crowd statistics system in the channel scene based on the existing research results. The main work of this paper includes: 1, human body moving target detection algorithm, combining fast three frame difference and head-shoulder contour features to quickly detect human moving target, through the detection of human body head-shoulder contour model, to realize the rapid detection of human body target. In order to meet the requirement of real time, the head-shoulder contour model detection method firstly extracts the head-shoulder contour feature, and matches the head-shoulder contour with the head-shoulder contour in the sample database. If the matching is successful, the target is judged as the human body target. Otherwise, it is a non-human target. 3, the human body moving target tracking algorithm, using the human body moving target detection stage to get the human head and shoulder contour, using the human head and shoulder tracking to replace the human body moving target tracking. By using the human head-shoulder contour feature and Kalman filter, the head-shoulder contour of the human body is matched with the sample in the head-shoulder sample database to track the head-shoulder of the human body. The minimum outer rectangle of the head and shoulder of the human body is used to replace the whole human body moving object. 4, and the tracking algorithm of multiple human moving targets is adopted. The algorithm deals with the occlusion, overlap and separation of multiple human moving targets in the process of motion. The algorithm can realize the tracking detection of multiple human moving targets. 5. The algorithm of crowd flow statistics can count the pedestrian targets passing through the counting line by setting the extended two-way counting line and realize the flow statistics of human body targets. The prototype system is developed on the VS2008 platform of Windows operating system using OpenCV library. The system development language is C, the hardware includes a camera, a notebook computer, in which the CPU main frequency 2.0GHz, memory 2G.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP391.41;TN948.6

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本文編號(hào)：2067458