【摘要】：首先本文具體介紹了運(yùn)動(dòng)目標(biāo)識(shí)別與跟蹤方法在國內(nèi)外的發(fā)展與研究的狀況,并且對(duì)相關(guān)的算法做了簡(jiǎn)要的說明。然后針對(duì)目標(biāo)識(shí)別與跟蹤方法,本文分別詳細(xì)介紹和說明了粒子濾波跟蹤算法與Camshift跟蹤算法,并對(duì)兩種跟蹤算法分別做了相應(yīng)的對(duì)比跟蹤實(shí)驗(yàn)來說明各自的特性及其優(yōu)缺點(diǎn)。對(duì)兩種跟蹤算法所適用的跟蹤環(huán)境進(jìn)行了分析,當(dāng)目標(biāo)與背景顏色差異比較大時(shí),粒子濾波跟蹤算法和Camshift算法能夠有效的跟蹤目標(biāo),但是當(dāng)目標(biāo)與背景顏色差異較小或者目標(biāo)處于復(fù)雜背景區(qū)域時(shí)目標(biāo)跟蹤就會(huì)產(chǎn)生偏差,甚至無法準(zhǔn)確的跟蹤目標(biāo)。為提高復(fù)雜背景下上述兩種跟蹤算法的穩(wěn)定性與準(zhǔn)確性,在兩種基本算法的基礎(chǔ)上,分別提出了各自的改進(jìn)算法來改善其在復(fù)雜背景下的跟蹤性能。提出基于顯著性直方圖模型的粒子濾波跟蹤方法。通過對(duì)比目標(biāo)與背景區(qū)中像素色調(diào)的分布,確定出不同色調(diào)等級(jí)的顯著性權(quán)值,從而建立起目標(biāo)的顯著性直方圖模型。顯著性直方圖模型可抑制背景中與目標(biāo)具有相似色調(diào)的區(qū)域?qū)δ繕?biāo)識(shí)別的干擾,突出目標(biāo)顯著色調(diào)在目標(biāo)識(shí)別中的作用,從而提高目標(biāo)識(shí)別的準(zhǔn)確性。提出了基于邊緣抑制的Camshift跟蹤算法。利用上一幀目標(biāo)的位置和大小通過權(quán)值函數(shù),在反向投影圖中降低目標(biāo)邊緣的亮度權(quán)值,被抑制的邊緣可以有效的區(qū)分目標(biāo)和背景,削弱質(zhì)心向背景方向迭代的趨勢(shì),提高目標(biāo)識(shí)別的準(zhǔn)確性。仿真實(shí)驗(yàn)結(jié)果表明,本文提出的兩種算法都能改善目標(biāo)跟蹤的準(zhǔn)確性和穩(wěn)定性,且計(jì)算量增加不多,能夠滿足電視跟蹤系統(tǒng)實(shí)時(shí)性的要求。最后將本文提出的兩種改進(jìn)跟蹤算法應(yīng)用到智能小車的跟蹤中,實(shí)驗(yàn)表明本文提出的改進(jìn)算法在實(shí)際應(yīng)用中可以獲得較好的跟蹤效果。
[Abstract]:Firstly, this paper introduces the development and research status of moving target recognition and tracking methods at home and abroad, and gives a brief description of the relevant algorithms. Then, the particle filter tracking algorithm and the Camshift tracking algorithm are introduced and explained in detail, and the two tracking algorithms are compared with each other in order to explain their characteristics and advantages and disadvantages. This paper analyzes the tracking environment of the two tracking algorithms. When the color difference between the target and the background is large, the particle filter tracking algorithm and the Camshift algorithm can effectively track the target. However, when the color difference between the target and the background is small or the target is in the complex background area, the target tracking will produce deviation, and even can not track the target accurately. In order to improve the stability and accuracy of the above two tracking algorithms in complex background, based on the two basic algorithms, their respective improved algorithms are proposed to improve their tracking performance in complex background. A particle filter tracking method based on significant histogram model is proposed. By comparing the distribution of pixel hue in the target and background region, the significance weights of different hue levels are determined, and the significance histogram model of the target is established. The significant histogram model can suppress the interference of the region with similar hue to the target recognition in the background and highlight the role of the significant hue of the target in target recognition so as to improve the accuracy of target recognition. A Camshift tracking algorithm based on edge suppression is proposed. By using the position and size of the object in the previous frame through the weight function, the brightness weight of the edge of the object is reduced in the reverse projection, and the suppressed edge can effectively distinguish the object from the background, and weaken the tendency of centroid iterating towards the background. Improve the accuracy of target recognition. The simulation results show that the two algorithms proposed in this paper can improve the accuracy and stability of target tracking, and the amount of computation is not much increased, which can meet the real-time requirements of TV tracking system. Finally, the two improved tracking algorithms proposed in this paper are applied to the tracking of intelligent cars. The experimental results show that the proposed improved tracking algorithm can obtain better tracking results in practical applications.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王紅茹;童偉;;基于自適應(yīng)高斯模型的實(shí)效運(yùn)動(dòng)目標(biāo)檢測(cè)算法[J];計(jì)算機(jī)工程與設(shè)計(jì);2016年10期

2 左軒塵;韓亮亮;莊杰;石琪琦;黃煒;;基于ROS的空間機(jī)器人人機(jī)交互系統(tǒng)設(shè)計(jì)[J];計(jì)算機(jī)工程與設(shè)計(jì);2015年12期

3 吳群;王田;王漢武;賴永炫;鐘必能;陳永紅;;現(xiàn)代智能視頻監(jiān)控研究綜述[J];計(jì)算機(jī)應(yīng)用研究;2016年06期

4 修春波;魏世安;;顯著性直方圖模型的Camshift跟蹤方法[J];光學(xué)精密工程;2015年06期

5 修春波;魏世安;萬蓉鳳;;二維聯(lián)合特征模型的自適應(yīng)均值漂移目標(biāo)跟蹤[J];光電子·激光;2015年02期

6 劉曉悅;孟妍;;運(yùn)動(dòng)目標(biāo)檢測(cè)與跟蹤算法的研究[J];河北聯(lián)合大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年01期

7 高雅;李曉娟;關(guān)永;王瑞;張杰;魏洪興;;運(yùn)用定理證明器ACL2驗(yàn)證機(jī)器人操作系統(tǒng)ROS節(jié)點(diǎn)間通信[J];小型微型計(jì)算機(jī)系統(tǒng);2014年09期

8 黃凱奇;陳曉棠;康運(yùn)鋒;譚鐵牛;;智能視頻監(jiān)控技術(shù)綜述[J];計(jì)算機(jī)學(xué)報(bào);2015年06期

9 王法勝;魯明羽;趙清杰;袁澤劍;;粒子濾波算法[J];計(jì)算機(jī)學(xué)報(bào);2014年08期

10 郭靜;羅華;張濤;;機(jī)器視覺與應(yīng)用[J];電子科技;2014年07期

相關(guān)博士學(xué)位論文 前1條

1 袁國武;智能視頻監(jiān)控中的運(yùn)動(dòng)目標(biāo)檢測(cè)和跟蹤算法研究[D];云南大學(xué);2012年

相關(guān)碩士學(xué)位論文 前3條

1 張潤(rùn);基于雙目立體視覺的帶鋼偏移測(cè)量系統(tǒng)研究[D];武漢理工大學(xué);2014年

2 彭麗玲;基于圖像灰度的天氣變化機(jī)理及應(yīng)用初步研究[D];昆明理工大學(xué);2013年

3 張麗;軍事運(yùn)動(dòng)目標(biāo)的識(shí)別與跟蹤研究[D];東北大學(xué);2009年

，

本文編號(hào)：2406516