基于協(xié)同分割的多視頻目標提取算法研究
發(fā)布時間：2019-02-23 23:51
【摘要】：隨著智能手機及平板電腦等電子產(chǎn)品的普及,以及微信、微博等媒體傳播平臺的快速發(fā)展,圖像和視頻已經(jīng)越來越深入地影響人類的生活方式。其中,視頻作為最豐富的信息載體之一,在各個行業(yè)中有著廣泛應用。隨著視頻信息的爆發(fā)式增長,如何讓計算機理解視頻場景內(nèi)容,并快速有效地提取人們所需的信息等問題變得越來越重要。其中,視頻分割作為視頻內(nèi)容分析的基本步驟,對視頻信息后期處理起到了關鍵作用,因此也受到了越來越多研究學者的關注。另外,視頻協(xié)同分割作為一項更具有挑戰(zhàn)性的課題,通過視頻間的特征一致性,將視頻集合中的共同目標進行分割,極大地改善了單個視頻的分割效果。本文主要圍繞視頻目標分割領域的一些問題進行了研究,采取合適的協(xié)同分割模型,建立視頻集合間的聯(lián)系,解決了視頻中非相關幀(幀內(nèi)不包含目標)的干擾問題,以及通過獲取具有全局一致性的目標跟蹤鏈對視頻協(xié)同分割提供可靠的先驗信息。具體的研究工作如下:一,對視頻分割中所涉及的幾種關鍵技術進行了介紹,詳細介紹了后文中所采用的可能目標區(qū)域生成方法,顯著性提取算法,并分別對幾種不同的方法進行了對比分析,詳細介紹了基于圖像協(xié)同的多目標搜索機制及基于光流的運動信息提取方法,為后文提出新的視頻協(xié)同分割方法提供理論基礎;二,提出了基于目標類選取及非相關幀檢測的多視頻協(xié)同分割方法。該方法中提出首先對視頻幀生成多樣化的可能目標區(qū)域候選集,并將其聚類劃分為多個目標類別,其次構(gòu)建基于目標類的圖模型,篩選出視頻間共同包含的目標,最后采用非相關幀判別機制及圖割優(yōu)化框架,實現(xiàn)非相關幀的篩選及相關幀的目標分割;三,提出了基于目標跟蹤鏈的視頻多目標協(xié)同分割方法。利用前后幀之間的相關性及全局一致性,對目標區(qū)域進行跟蹤并獲取目標跟蹤鏈,從而得到較為可靠的目標運動軌跡及先驗信息,并采用能量優(yōu)化框架完成最終共同目標的協(xié)同分割及多類目標分割。
[Abstract]:With the popularity of electronic products such as smart phones and tablets, as well as the rapid development of WeChat, Weibo and other media communication platforms, images and video have more and more profound impact on the human way of life. Among them, as one of the most abundant information carriers, video is widely used in various industries. With the explosive growth of video information, how to make the computer understand the content of video scene and extract the information that people need quickly and effectively becomes more and more important. As the basic step of video content analysis, video segmentation plays a key role in the post-processing of video information, so it has attracted more and more researchers' attention. In addition, as a more challenging subject, video co-segmentation can greatly improve the segmentation effect of a single video by using the feature consistency of video to segment the common target in the video set. This paper mainly focuses on some problems in video target segmentation field, adopts appropriate cooperative segmentation model, establishes the relationship between video sets, and solves the interference problem of non-correlated frames (no target included in frames). And the target tracking chain with global consistency can provide reliable prior information for video cooperative segmentation. The specific research work is as follows: firstly, several key technologies involved in video segmentation are introduced, and the methods of generating possible target regions and salience extraction algorithm are introduced in detail. Several different methods are compared and analyzed respectively. The mechanism of multi-object search based on image collaboration and the method of motion information extraction based on optical flow are introduced in detail. Secondly, a multi-video cooperative segmentation method based on target class selection and uncorrelated frame detection is proposed. In this method, first of all, the possible target region candidate sets are generated for the video frames, and the candidate sets are clustered into multiple target categories. Secondly, a graph model based on the target class is constructed to screen out the targets that are included among the video frames. Finally, uncorrelated frame selection mechanism and graph cut optimization framework are used to realize the selection of uncorrelated frames and the target segmentation of correlated frames. Thirdly, a video multi-target cooperative segmentation method based on target tracking chain is proposed. By using the correlation and global consistency between the frames, the target region is tracked and the target tracking chain is obtained, so as to obtain more reliable target track and prior information. And the energy optimization framework is used to complete the cooperative segmentation and multi-class target segmentation of the final common goal.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
[Abstract]:With the popularity of electronic products such as smart phones and tablets, as well as the rapid development of WeChat, Weibo and other media communication platforms, images and video have more and more profound impact on the human way of life. Among them, as one of the most abundant information carriers, video is widely used in various industries. With the explosive growth of video information, how to make the computer understand the content of video scene and extract the information that people need quickly and effectively becomes more and more important. As the basic step of video content analysis, video segmentation plays a key role in the post-processing of video information, so it has attracted more and more researchers' attention. In addition, as a more challenging subject, video co-segmentation can greatly improve the segmentation effect of a single video by using the feature consistency of video to segment the common target in the video set. This paper mainly focuses on some problems in video target segmentation field, adopts appropriate cooperative segmentation model, establishes the relationship between video sets, and solves the interference problem of non-correlated frames (no target included in frames). And the target tracking chain with global consistency can provide reliable prior information for video cooperative segmentation. The specific research work is as follows: firstly, several key technologies involved in video segmentation are introduced, and the methods of generating possible target regions and salience extraction algorithm are introduced in detail. Several different methods are compared and analyzed respectively. The mechanism of multi-object search based on image collaboration and the method of motion information extraction based on optical flow are introduced in detail. Secondly, a multi-video cooperative segmentation method based on target class selection and uncorrelated frame detection is proposed. In this method, first of all, the possible target region candidate sets are generated for the video frames, and the candidate sets are clustered into multiple target categories. Secondly, a graph model based on the target class is constructed to screen out the targets that are included among the video frames. Finally, uncorrelated frame selection mechanism and graph cut optimization framework are used to realize the selection of uncorrelated frames and the target segmentation of correlated frames. Thirdly, a video multi-target cooperative segmentation method based on target tracking chain is proposed. By using the correlation and global consistency between the frames, the target region is tracked and the target tracking chain is obtained, so as to obtain more reliable target track and prior information. And the energy optimization framework is used to complete the cooperative segmentation and multi-class target segmentation of the final common goal.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
【相似文獻】
相關期刊論文 前10條
1 靳曉輝;候正信;;智能監(jiān)控系統(tǒng)中運動目標分割算法[J];電子測量技術;2005年06期
2 徐軍，，向健勇，周翔，楊宜禾;基于特征域的目標分割算法[J];紅外與激光工程;1998年02期
3 袁天云,姜志國,孟如松;目標分割圖中粘連對象的自動切割和分離[J];中國體視學與圖像分析;2003年01期
4 李小俊;姚馳甫;;背景重建的運動目標分割改進算法[J];計算機工程與應用;2009年27期
5 鄧穎娜;朱虹;劉薇;錢慧芳;;基于姿態(tài)模型的人群目標分割方法[J];計算機工程;2010年07期
6 李晶晶;管業(yè)鵬;葉勇;;復雜背景下的運動目標分割與陰影消除[J];電子器件;2011年05期
7 李林;盧煥章;肖山竹;廖斌;;基于部分投影的粘連目標分割[J];電視技術;2013年11期
8 侯格賢,吳成柯;一種結(jié)合遺傳算法的自適應目標分割方法[J];西安電子科技大學學報;1998年02期
9 李艷,彭嘉雄;基于分維特征的目標分割與檢測[J];華中理工大學學報;2000年08期
10 楊益軍,趙榮椿;一個快速的人機交互式目標分割系統(tǒng)[J];西北工業(yè)大學學報;2000年02期
相關會議論文 前5條
1 吳明軍;;自由運動像機下的運動目標分割[A];豫贛黑蘇魯五省光學（激光）學會聯(lián)合學術2012年會論文摘要集[C];2012年
2 袁天云;姜志國;;目標分割圖中粘連對象的自動切割和分離[A];圖像 仿真 信息技術——第二屆聯(lián)合學術會議論文集[C];2002年
3 劉o
本文編號：2429349
本文鏈接：http://www.sikaile.net/kejilunwen/ruanjiangongchenglunwen/2429349.html
最近更新
教材專著
