【摘要】：運(yùn)動(dòng)目標(biāo)檢測(cè)與跟蹤是計(jì)算機(jī)視覺(jué)中的一個(gè)熱點(diǎn)問(wèn)題,并在智能監(jiān)控系統(tǒng)中越來(lái)越受到人們的重視。根據(jù)攝像機(jī)與監(jiān)控背景之間的相對(duì)運(yùn)動(dòng)關(guān)系,將目標(biāo)運(yùn)動(dòng)分為靜態(tài)背景下的與動(dòng)態(tài)背景下的兩類(lèi)運(yùn)動(dòng)。目前,靜態(tài)背景下的運(yùn)動(dòng)目標(biāo)研究已經(jīng)相對(duì)成熟,并廣泛的應(yīng)用于社會(huì)的各個(gè)領(lǐng)域,而動(dòng)態(tài)背景下的目標(biāo)運(yùn)動(dòng)相對(duì)復(fù)雜,理論與實(shí)際應(yīng)用方面還有很多問(wèn)題需要解決,本文主要對(duì)動(dòng)態(tài)背景下的行人目標(biāo)進(jìn)行檢測(cè)與跟蹤研究,通過(guò)實(shí)驗(yàn)驗(yàn)證本文算法的可行性。本文首先對(duì)行人檢測(cè)與跟蹤的現(xiàn)狀進(jìn)行了綜述,分析了行人檢測(cè)與跟蹤過(guò)程中可能遇到的各個(gè)方面的問(wèn)題,并結(jié)合數(shù)字圖像處理的知識(shí),分析了目標(biāo)檢測(cè)在靜態(tài)背景與動(dòng)態(tài)背景兩種情況下的主要提取方法。動(dòng)態(tài)背景下目標(biāo)的運(yùn)動(dòng)會(huì)受到相機(jī)運(yùn)動(dòng)產(chǎn)生的全局運(yùn)動(dòng)的干擾,需要我們對(duì)相機(jī)的運(yùn)動(dòng)進(jìn)行背景補(bǔ)償。本文通過(guò)SIFT特征點(diǎn)進(jìn)行點(diǎn)匹配,完成仿射參數(shù)模型的參數(shù)估計(jì),在估計(jì)過(guò)程中,提出了一種新的間隔匹配的方法去除特征點(diǎn)中外點(diǎn)的干擾。在目標(biāo)提取中,采用多幀差分的方法增強(qiáng)目標(biāo)檢測(cè)的判別能力,很好的滿(mǎn)足了檢測(cè)的需要。針對(duì)行人目標(biāo)的跟蹤問(wèn)題,本文首先介紹了幾種傳統(tǒng)的目標(biāo)跟蹤方法,分析了幾種算法的不足之處,并應(yīng)用Camshift算法進(jìn)行驗(yàn)證,得出單一的特征很難在復(fù)雜的環(huán)境下實(shí)現(xiàn)目標(biāo)的描述,容易發(fā)生目標(biāo)丟失的情況。本文提出了多特征融合的方法對(duì)跟蹤算計(jì)進(jìn)行改進(jìn),針對(duì)不同環(huán)境下目標(biāo)跟蹤的需求,可以選擇不同的特征進(jìn)行融合,改進(jìn)后的跟蹤算法能夠很好的實(shí)現(xiàn)目標(biāo)的有效跟蹤,提高了算法的魯棒性。動(dòng)態(tài)背景下的目標(biāo)跟蹤具有廣闊的應(yīng)用前景,在智能汽車(chē)、無(wú)人機(jī)拍攝、特種作戰(zhàn)等領(lǐng)域都能夠得到很好的應(yīng)用。本文主要對(duì)移動(dòng)設(shè)備上實(shí)現(xiàn)行人目標(biāo)的自動(dòng)檢測(cè)與跟蹤進(jìn)行了分析,通過(guò)實(shí)驗(yàn)結(jié)果驗(yàn)證系統(tǒng)的可行性,結(jié)果表明,本文提出的算法能夠滿(mǎn)足移動(dòng)設(shè)備上行人自動(dòng)檢測(cè)與跟蹤的要求,具有一定的實(shí)用價(jià)值。
[Abstract]:Moving target detection and tracking is a hot issue in computer vision, and more attention has been paid to it in intelligent monitoring system. According to the relative motion relationship between camera and monitoring background, the target motion is divided into two kinds of motion in static background and dynamic background. At present, the research of moving target in static background has been relatively mature and widely used in various fields of society, while the moving of target in dynamic background is relatively complex, and there are still many problems to be solved in theory and practical application. In this paper, the detection and tracking of pedestrian targets in dynamic background are mainly studied, and the feasibility of the algorithm is verified by experiments. In this paper, the present situation of pedestrian detection and tracking is reviewed, and the problems that may be encountered in the process of pedestrian detection and tracking are analyzed. Combined with the knowledge of digital image processing, the main extraction methods of target detection in static background and dynamic background are analyzed. Under the dynamic background, the motion of the target is interfered by the global motion caused by the motion of the camera, so we need to compensate the background of the motion of the camera. In this paper, the parameter estimation of affine parameter model is completed by point matching of SIFT feature points. in the process of estimation, a new interval matching method is proposed to remove the interference of external points in feature points. In target extraction, multi-frame difference method is used to enhance the discriminant ability of target detection, which meets the needs of detection. Aiming at the problem of pedestrian target tracking, this paper first introduces several traditional target tracking methods, analyzes the shortcomings of several algorithms, and verifies them with Camshift algorithm. It is concluded that a single feature is difficult to achieve the target description in a complex environment, and the target loss is easy to occur. In this paper, a multi-feature fusion method is proposed to improve the tracking algorithm. According to the requirements of target tracking in different environments, different features can be selected for fusion. The improved tracking algorithm can achieve effective target tracking and improve the robustness of the algorithm. Target tracking in dynamic background has a broad application prospect, and can be well applied in intelligent vehicles, UAV photography, special operations and other fields. In this paper, the automatic detection and tracking of pedestrian targets on mobile devices is analyzed, and the feasibility of the system is verified by experimental results. The results show that the algorithm proposed in this paper can meet the requirements of automatic detection and tracking of pedestrians on mobile devices, and has certain practical value.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王大溪;胡鵬;;改進(jìn)鄰域平均法在MRI圖像去噪處理中的研究[J];電子科技;2014年08期

2 周帆;江維;李樹(shù)全;張玉宏;曾雪;吳躍;;基于粒子濾波的移動(dòng)物體定位和追蹤算法[J];軟件學(xué)報(bào);2013年09期

3 肖大雪;;淺析數(shù)學(xué)形態(tài)學(xué)在圖像處理中的應(yīng)用[J];科技廣場(chǎng);2013年05期

4 袁國(guó)武;陳志強(qiáng);龔健;徐丹;廖仁健;何俊遠(yuǎn);;一種結(jié)合光流法與三幀差分法的運(yùn)動(dòng)目標(biāo)檢測(cè)算法[J];小型微型計(jì)算機(jī)系統(tǒng);2013年03期

5 黃國(guó)航;;智能監(jiān)控技術(shù)研究現(xiàn)狀及應(yīng)用[J];湖北農(nóng)機(jī)化;2012年04期

6 蘇松志;李紹滋;陳淑媛;蔡國(guó)榕;吳云東;;行人檢測(cè)技術(shù)綜述[J];電子學(xué)報(bào);2012年04期

7 馮珂;朱敏;鐘煜;范量;;一種改進(jìn)的Canny邊緣檢測(cè)AGT算法[J];計(jì)算機(jī)應(yīng)用與軟件;2012年03期

8 賀柏根;朱明;;改進(jìn)的抗全仿射尺度不變特征變換圖像匹配算法[J];光學(xué)精密工程;2011年10期

9 馬曉婷;;行人跟蹤技術(shù)綜述[J];電腦知識(shí)與技術(shù);2011年19期

10 王國(guó)良;劉金國(guó);;基于粒子濾波的多自由度運(yùn)動(dòng)目標(biāo)跟蹤[J];光學(xué)精密工程;2011年04期

相關(guān)碩士學(xué)位論文 前3條

1 宋亞姬;基于卡爾曼濾波的運(yùn)動(dòng)目標(biāo)檢測(cè)與跟蹤技術(shù)研究[D];長(zhǎng)春理工大學(xué);2012年

2 田莘;基于MeanShift算法的目標(biāo)跟蹤問(wèn)題研究[D];西安科技大學(xué);2010年

3 陳鋒強(qiáng);復(fù)雜場(chǎng)景下運(yùn)動(dòng)物體檢測(cè)與跟蹤算法的研究[D];上海交通大學(xué);2008年

，

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