基于背景建模和屬性學(xué)習(xí)的視頻摘要研究
發(fā)布時間：2018-10-16 16:33
【摘要】：隨著高清攝像設(shè)備的普及和物聯(lián)網(wǎng)的興起以及平安城市和智慧城市的提出,監(jiān)控攝像頭被廣泛地部署在城市的每一個角落。監(jiān)控設(shè)備可以在打擊違法犯罪,維護(hù)社會長治久安上發(fā)揮重要的作用。然而海量的視頻數(shù)據(jù)也在視頻的存儲歸檔和查閱檢索上給人們帶來巨大的考驗。傳統(tǒng)的直接存儲和人工檢索方式已經(jīng)無法應(yīng)對大規(guī)模視頻的處理需求。如何解決海量視頻的存儲和檢索的難題已經(jīng)成為國內(nèi)外學(xué)者研究的熱點(diǎn)。因此本文針對這兩個難題展開了相關(guān)研究。在查閱了大量國內(nèi)外文獻(xiàn)和資料之后,對視頻存儲和檢索領(lǐng)域有了一定的了解,深入分析了課題的研究現(xiàn)狀。闡述了當(dāng)前研究工作的主要難點(diǎn)在于如何將監(jiān)控視頻中前景對象準(zhǔn)確且無遺漏地檢測出來;在檢測出前景后如何對其進(jìn)行多概念檢測;在對多概念對象進(jìn)行分類和描述時如何跨越語義鴻溝等。在此基礎(chǔ)上本文提出了基于背景檢測和屬性學(xué)習(xí)的視頻摘要方法。利用改進(jìn)后的ViBe對視頻序列進(jìn)行背景建模,去除不包含前景對象的視頻幀,將其余幀保留下來生成濃縮后的視頻,以達(dá)到減少視頻文件對存儲造成的壓力的目的;在獲取到前景對象后建立屬性分類器,利用屬性學(xué)習(xí)對前景對象進(jìn)行概念檢測,檢測出相應(yīng)概念后利用屬性標(biāo)簽來描述該前景對象,由此在濃縮的視頻基礎(chǔ)上生成視頻摘要。本文研究的主要內(nèi)容如下:(1)提出了基于改進(jìn)ViBe的視頻背景建模與濃縮。在對視頻背景建模算法進(jìn)行研究對比后,選擇較其他主流方法速度快、占用內(nèi)存少的ViBe算法。針對原ViBe算法在實(shí)際監(jiān)控場景下仍會存在噪點(diǎn)和閃爍點(diǎn)以及在初始化過程中會引入鬼影的問題,對ViBe算法進(jìn)行改進(jìn),分別提出了基于計數(shù)點(diǎn)閾值的閃爍點(diǎn)去除方法,基于形態(tài)學(xué)的噪點(diǎn)消除方法,和面向鬼影區(qū)域檢測和抑制的改進(jìn)算法。在實(shí)現(xiàn)并實(shí)驗驗證了對ViBe的改進(jìn)后,將其應(yīng)用于前景提取與視頻濃縮中去。首先對視頻進(jìn)行背景建模,獲取前景對象。而后將不包含前景對象的無用幀略去,以達(dá)到去除時間維度上的冗余信息的目的,對視頻進(jìn)行濃縮。(2)提出了基于多核屬性學(xué)習(xí)的前景多概念檢測與摘要。首先將多核學(xué)習(xí)引入直接屬性預(yù)測模型框架中,給出了對核函數(shù)的權(quán)重向量進(jìn)行優(yōu)化求解方法;進(jìn)一步地,將提出的模型運(yùn)用視頻對象分類中;繼而利用模型的多概念分類能力和屬性描述能力,對監(jiān)控視頻前景多概念進(jìn)行檢測,并給檢測出的對象加上屬性標(biāo)簽,生成視頻摘要;最后,設(shè)計對比實(shí)驗對提出方法的有效性進(jìn)行驗證。(3)在前面兩個研究點(diǎn)的基礎(chǔ)上,運(yùn)用軟件工程中面向?qū)ο蟮乃悸反罱ɑ诒尘敖：蛯傩詫W(xué)習(xí)的視頻摘要原型系統(tǒng)。系統(tǒng)包含視頻濃縮模塊、屬性預(yù)測模型訓(xùn)練模塊、視頻摘要模塊。運(yùn)行效果良好,達(dá)到了本研究的預(yù)期目標(biāo)。
[Abstract]:With the popularization of high-definition camera equipment and the rise of Internet of things and the introduction of Ping'an City and Smart City, surveillance cameras are widely deployed in every corner of the city. Monitoring equipment can play an important role in cracking down on crime and maintaining social stability. However, the huge amount of video data also brings people a great test in the storage, archiving and retrieval of video. Traditional methods of direct storage and manual retrieval can no longer cope with the need of large-scale video processing. How to solve the problem of mass video storage and retrieval has become a hot topic for scholars at home and abroad. Therefore, this paper has carried out the related research in view of these two difficult problems. After consulting a large number of domestic and foreign literature and materials, we have a certain understanding of video storage and retrieval field, in-depth analysis of the research status of the subject. The main difficulties of the current research work are how to detect the foreground objects accurately and without omission, how to detect the foreground objects accurately and how to detect them with multiple concepts after detecting the foreground. How to cross the semantic gap when classifying and describing multi-concept objects. On this basis, this paper proposes a video summarization method based on background detection and attribute learning. The improved ViBe is used to model the background of the video sequence, remove the video frames without foreground objects, and save the remaining frames to generate the condensed video, so as to reduce the pressure caused by the video files on the storage. After obtaining the foreground object, the attribute classifier is established, and the concept of foreground object is detected by using attribute learning, and then the foreground object is described by attribute label, and the video summary is generated on the basis of condensed video. The main contents of this paper are as follows: (1) the video background modeling and concentration based on improved ViBe is proposed. After studying and comparing the video background modeling algorithm, the ViBe algorithm, which is faster than other mainstream methods and occupies less memory, is selected. In view of the problem that the original ViBe algorithm still has noise and flicker points in the actual monitoring scene and the ghosts will be introduced in the initialization process, the ViBe algorithm is improved, and the method of removing the flashing points based on the count point threshold is proposed respectively. Morphology based noise cancellation method, and an improved algorithm for ghost region detection and suppression. After the implementation and experimental verification of the improved ViBe, it is applied to foreground extraction and video concentration. Firstly, the background of the video is modeled and the foreground object is obtained. Then the useless frame without foreground object is omitted to remove redundant information in time dimension and the video is condensed. (2) Multi-concept detection and summary of foreground based on multi-core attribute learning is proposed. Firstly, multi-kernel learning is introduced into the framework of direct attribute prediction model, and the optimization method of weight vector of kernel function is given. Furthermore, the proposed model is applied to video object classification. Then, the multi-concept classification ability and attribute description ability of the model are used to detect the multi-concept of the surveillance video foreground, and the detected objects are tagged with attributes to generate the video summary. A comparative experiment is designed to verify the effectiveness of the proposed method. (3) on the basis of the above two research points, a video abstract prototype system based on background modeling and attribute learning is built by using the object-oriented approach in software engineering. The system includes video enrichment module, attribute prediction model training module and video summary module. The operation effect is good and the expected goal of this study has been achieved.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2274982
[Abstract]:With the popularization of high-definition camera equipment and the rise of Internet of things and the introduction of Ping'an City and Smart City, surveillance cameras are widely deployed in every corner of the city. Monitoring equipment can play an important role in cracking down on crime and maintaining social stability. However, the huge amount of video data also brings people a great test in the storage, archiving and retrieval of video. Traditional methods of direct storage and manual retrieval can no longer cope with the need of large-scale video processing. How to solve the problem of mass video storage and retrieval has become a hot topic for scholars at home and abroad. Therefore, this paper has carried out the related research in view of these two difficult problems. After consulting a large number of domestic and foreign literature and materials, we have a certain understanding of video storage and retrieval field, in-depth analysis of the research status of the subject. The main difficulties of the current research work are how to detect the foreground objects accurately and without omission, how to detect the foreground objects accurately and how to detect them with multiple concepts after detecting the foreground. How to cross the semantic gap when classifying and describing multi-concept objects. On this basis, this paper proposes a video summarization method based on background detection and attribute learning. The improved ViBe is used to model the background of the video sequence, remove the video frames without foreground objects, and save the remaining frames to generate the condensed video, so as to reduce the pressure caused by the video files on the storage. After obtaining the foreground object, the attribute classifier is established, and the concept of foreground object is detected by using attribute learning, and then the foreground object is described by attribute label, and the video summary is generated on the basis of condensed video. The main contents of this paper are as follows: (1) the video background modeling and concentration based on improved ViBe is proposed. After studying and comparing the video background modeling algorithm, the ViBe algorithm, which is faster than other mainstream methods and occupies less memory, is selected. In view of the problem that the original ViBe algorithm still has noise and flicker points in the actual monitoring scene and the ghosts will be introduced in the initialization process, the ViBe algorithm is improved, and the method of removing the flashing points based on the count point threshold is proposed respectively. Morphology based noise cancellation method, and an improved algorithm for ghost region detection and suppression. After the implementation and experimental verification of the improved ViBe, it is applied to foreground extraction and video concentration. Firstly, the background of the video is modeled and the foreground object is obtained. Then the useless frame without foreground object is omitted to remove redundant information in time dimension and the video is condensed. (2) Multi-concept detection and summary of foreground based on multi-core attribute learning is proposed. Firstly, multi-kernel learning is introduced into the framework of direct attribute prediction model, and the optimization method of weight vector of kernel function is given. Furthermore, the proposed model is applied to video object classification. Then, the multi-concept classification ability and attribute description ability of the model are used to detect the multi-concept of the surveillance video foreground, and the detected objects are tagged with attributes to generate the video summary. A comparative experiment is designed to verify the effectiveness of the proposed method. (3) on the basis of the above two research points, a video abstract prototype system based on background modeling and attribute learning is built by using the object-oriented approach in software engineering. The system includes video enrichment module, attribute prediction model training module and video summary module. The operation effect is good and the expected goal of this study has been achieved.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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