【摘要】：隨著高清攝像設備的普及和物聯(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)控設備可以在打擊違法犯罪,維護社會長治久安上發(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)外學者研究的熱點。因此本文針對這兩個難題展開了相關研究。在查閱了大量國內(nèi)外文獻和資料之后,對視頻存儲和檢索領域有了一定的了解,深入分析了課題的研究現(xiàn)狀。闡述了當前研究工作的主要難點在于如何將監(jiān)控視頻中前景對象準確且無遺漏地檢測出來;在檢測出前景后如何對其進行多概念檢測;在對多概念對象進行分類和描述時如何跨越語義鴻溝等。在此基礎上本文提出了基于背景檢測和屬性學習的視頻摘要方法。利用改進后的ViBe對視頻序列進行背景建模,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,利用屬性學習對前景對象進行概念檢測,檢測出相應概念后利用屬性標簽來描述該前景對象,由此在濃縮的視頻基礎上生成視頻摘要。本文研究的主要內(nèi)容如下:(1)提出了基于改進ViBe的視頻背景建模與濃縮。在對視頻背景建模算法進行研究對比后,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算法在實際監(jiān)控場景下仍會存在噪點和閃爍點以及在初始化過程中會引入鬼影的問題,對ViBe算法進行改進,分別提出了基于計數(shù)點閾值的閃爍點去除方法,基于形態(tài)學的噪點消除方法,和面向鬼影區(qū)域檢測和抑制的改進算法。在實現(xiàn)并實驗驗證了對ViBe的改進后,將其應用于前景提取與視頻濃縮中去。首先對視頻進行背景建模,獲取前景對象。而后將不包含前景對象的無用幀略去,以達到去除時間維度上的冗余信息的目的,對視頻進行濃縮。(2)提出了基于多核屬性學習的前景多概念檢測與摘要。首先將多核學習引入直接屬性預測模型框架中,給出了對核函數(shù)的權重向量進行優(yōu)化求解方法;進一步地,將提出的模型運用視頻對象分類中;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行檢測,并給檢測出的對象加上屬性標簽,生成視頻摘要;最后,設計對比實驗對提出方法的有效性進行驗證。(3)在前面兩個研究點的基礎上,運用軟件工程中面向對象的思路搭建基于背景建模和屬性學習的視頻摘要原型系統(tǒng)。系統(tǒng)包含視頻濃縮模塊、屬性預測模型訓練模塊、視頻摘要模塊。運行效果良好,達到了本研究的預期目標。
[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.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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