本文選題：監(jiān)控視頻檢索 + 鏡頭分割��； 參考：《山西大學》2014年碩士論文

【摘要】：視頻監(jiān)控是通過攝像頭來獲取一定區(qū)域的視頻圖像信息,以實現對進入該區(qū)域范圍內的目標及其行為動態(tài)進行監(jiān)督的目的。目前,視頻監(jiān)控已經日益廣泛地應用在國計民生的多個領域中,如安防、交通、軍事等。視頻監(jiān)控系統(tǒng)產生了海量的視頻文件,由于視頻文件數據量大、結構復雜、表現形式多樣化,人們使用傳統(tǒng)的基于文本標記的瀏覽和檢索方式來訪問監(jiān)控視頻,無論在時耗和精度上都很難滿足實際工作的需求。針對這一問題,本文對一種高效的瀏覽、檢索監(jiān)控視頻的算法即基于內容的監(jiān)控視頻檢索算法展開研究。本文在研究基于內容的視頻檢索算法基本理論的基礎上,結合視頻監(jiān)控場景的特點和人們的檢索需求,重點對鏡頭分割、關鍵幀提取、關鍵幀檢索匹配等視頻檢索中的關鍵技術進行研究。主要的研究內容如下文所述：(1)研究分析基于內容的視頻檢索算法的應用現狀和發(fā)展前景,回顧、展望該領域的國內外發(fā)展動態(tài),對其基礎理論知識和一些常用的檢索算法進行研究分析。(2)結合視頻監(jiān)控場景的特點和實際需要,研究提出了一種基于灰度變化檢測的鏡頭分割算法。通過設定虛擬檢測線,統(tǒng)計計算虛擬檢測線路徑上灰度變化來確定鏡頭的開始；計算目標前景總灰度值,當其減小到一定值時,鏡頭結束。以此獲得一個完整的鏡頭。(3)研究關鍵幀提取方法,在提取監(jiān)控視頻鏡頭關鍵幀時,首先合理的選取第一個關鍵幀,再統(tǒng)計邊緣方向直方圖、計算幀間差來更新、獲取其余的關鍵幀。(4)研究關鍵幀的檢索匹配算法,研究提出一種基于邊緣方向直方圖相關性匹配的圖像檢索算法。對圖像進行去噪、提取邊緣后,計算獲取邊緣方向直方圖,等級化排列直方圖構成特征向量。再使用斯皮爾曼等級相關公式計算圖像特征向量間的相關系數作為衡量圖像間相似性的指標。通過實驗對算法的有效性、可靠性進行驗證。(5)鑒于人們常關注監(jiān)控視頻中目標的顏色、形狀信息,研究了一種綜合使用顏色特征和形狀特征的關鍵幀匹配算法并通過實驗驗證了算法的性能。最后,結合網站開發(fā)相關技術和本文研究的算法,研究開發(fā)一個在線的監(jiān)控視頻檢索系統(tǒng),用戶可以遠程登錄系統(tǒng)對視頻進行檢索。
[Abstract]:Video surveillance is to obtain the video image information of a certain area through the camera, in order to achieve the purpose of monitoring the target and its behavior in the region.At present, video surveillance has been widely used in many fields of national economy and people's livelihood, such as security, traffic, military and so on.Video surveillance system has produced a large number of video files. Because of the large amount of data, complex structure and diverse forms of expression, people use the traditional browsing and retrieval methods based on text markup to access the monitored video.It is difficult to meet the requirements of practical work in terms of time consumption and accuracy.In order to solve this problem, this paper studies an efficient browsing and retrieval algorithm for surveillance video, that is, content-based video retrieval algorithm.Based on the research of the basic theory of content-based video retrieval algorithm and the characteristics of video surveillance scene and people's retrieval requirements, this paper focuses on shot segmentation and key frame extraction.Key techniques in video retrieval such as key frame retrieval and matching are studied.The main research contents are as follows: (1) Research and analysis of the application status and development prospects of content-based video retrieval algorithms, review, and prospects for the development of this field at home and abroad.Based on the basic theoretical knowledge and some commonly used retrieval algorithms, a shot segmentation algorithm based on gray change detection is proposed, which is based on the characteristics and practical needs of video surveillance scene.By setting the virtual detection line, the grayscale change on the path of the virtual detection line is statistically calculated to determine the start of the shot, and the total gray value of the target foreground is calculated, when it is reduced to a certain value, the shot ends.In order to obtain a complete shot. 3) the key frame extraction method is studied. When extracting the key frame of surveillance video shot, the first key frame is selected reasonably, then the edge direction histogram is counted and the difference between frames is calculated to update.The key frame matching algorithm is studied, and an image retrieval algorithm based on edge direction histogram correlation matching is proposed.The image is de-noised, the edge is extracted, the edge direction histogram is obtained, and the hierarchical histogram is arranged to form the feature vector.Then the correlation coefficient between the image feature vectors is calculated by using the Spelman rank correlation formula as an index to measure the similarity between images.The validity and reliability of the algorithm are verified by experiments. (5) in view of the fact that people often pay attention to the color and shape information of the target in the surveillance video,A key frame matching algorithm which combines color features with shape features is studied and the performance of the algorithm is verified by experiments.Finally, an online surveillance video retrieval system is developed by combining the related technologies of website development and the algorithms studied in this paper. Users can remotely log on to the video retrieval system.
【學位授予單位】：山西大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN948.6

【參考文獻】

相關期刊論文 前5條

1 劉政凱,湯曉鷗;視頻檢索中鏡頭分割方法綜述[J];計算機工程與應用;2002年23期

2 何云峰;周玲;于俊清;徐濤;管濤;;基于局部特征聚合的圖像檢索方法[J];計算機學報;2011年11期

3 王思文;賈克斌;王純;劉帷;;基于運動信息的鏡頭切變檢測與關鍵幀提取[J];計算機工程;2012年16期

4 信師國;劉慶磊;劉全賓;;網絡視頻監(jiān)控系統(tǒng)現狀和發(fā)展趨勢[J];信息技術與信息化;2010年01期

5 魏瑋;劉靜;王丹丹;;視頻鏡頭分割方法綜述[J];計算機系統(tǒng)應用;2013年01期

相關碩士學位論文 前2條

1 黃韜;基于IP網絡的公安數字視頻監(jiān)控系統(tǒng)的研究與實現[D];南昌大學;2011年

2 張萌;視頻檢索中關鍵幀的提取和特征匹配的研究[D];北京郵電大學;2012年

，

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