發(fā)布時間：2018-04-18 12:07

  本文選題：監(jiān)控視頻檢索 + 鏡頭分割��； 參考：《山西大學(xué)》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(xiàn)對進入該區(qū)域范圍內(nèi)的目標(biāo)及其行為動態(tài)進行監(jiān)督的目的。目前,視頻監(jiān)控已經(jīng)日益廣泛地應(yīng)用在國計民生的多個領(lǐng)域中,如安防、交通、軍事等。視頻監(jiān)控系統(tǒng)產(chǎn)生了海量的視頻文件,由于視頻文件數(shù)據(jù)量大、結(jié)構(gòu)復(fù)雜、表現(xiàn)形式多樣化,人們使用傳統(tǒng)的基于文本標(biāo)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(nèi)容的監(jiān)控視頻檢索算法展開研究。本文在研究基于內(nèi)容的視頻檢索算法基本理論的基礎(chǔ)上,結(jié)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(guān)鍵幀提取、關(guān)鍵幀檢索匹配等視頻檢索中的關(guān)鍵技術(shù)進行研究。主要的研究內(nèi)容如下文所述：(1)研究分析基于內(nèi)容的視頻檢索算法的應(yīng)用現(xiàn)狀和發(fā)展前景,回顧、展望該領(lǐng)域的國內(nèi)外發(fā)展動態(tài),對其基礎(chǔ)理論知識和一些常用的檢索算法進行研究分析。(2)結(jié)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(shè)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(biāo)前景總灰度值,當(dāng)其減小到一定值時,鏡頭結(jié)束。以此獲得一個完整的鏡頭。(3)研究關(guān)鍵幀提取方法,在提取監(jiān)控視頻鏡頭關(guān)鍵幀時,首先合理的選取第一個關(guān)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(guān)鍵幀。(4)研究關(guān)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(guān)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(gòu)成特征向量。再使用斯皮爾曼等級相關(guān)公式計算圖像特征向量間的相關(guān)系數(shù)作為衡量圖像間相似性的指標(biāo)。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(guān)注監(jiān)控視頻中目標(biāo)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(guān)鍵幀匹配算法并通過實驗驗證了算法的性能。最后,結(jié)合網(wǎng)站開發(fā)相關(guān)技術(shù)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(xué)位授予單位】：山西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN948.6

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本文編號：1768310