視頻流中標版類廣告的檢測方法研究
發(fā)布時間：2019-05-20 14:29
【摘要】：隨著商業(yè)化步伐的加快，現(xiàn)代媒體領(lǐng)域中的廣告的元素日趨增多，媒體產(chǎn)業(yè)的發(fā)展也對視頻監(jiān)測工作提出了新的更高的要求。作為媒體市場監(jiān)管方的視頻監(jiān)測部門常常需要檢測視頻中某個廣告圖標出現(xiàn)的時間位置和存在的時間長度，以此規(guī)范各廣告商的經(jīng)營行為。因此，相關(guān)方面的技術(shù)已是當今傳媒領(lǐng)域的熱門，也是媒體產(chǎn)業(yè)得以健康發(fā)展的必要保證。 由于相關(guān)技術(shù)涉及圖像處理、圖像理解等新興領(lǐng)域，缺乏現(xiàn)有方法和理論依據(jù)，因此對上述技術(shù)的研究也具有相當?shù)碾y度。比如說，廣告圖標在視頻中的顯示常常伴隨有尺寸、旋轉(zhuǎn)、光照等變化，若使用依據(jù)像素的比較方法或基于內(nèi)容的圖像檢測方法則無法適應(yīng)如此多樣的變化。另外，視頻作為圖像在時間軸上的序列，增加時間維度后也為檢測工作增加了難度。方法和理論的匱乏阻礙了相關(guān)領(lǐng)域的研究，也阻礙了相關(guān)產(chǎn)業(yè)的發(fā)展。而本文以SURF方法為理論基礎(chǔ)和出發(fā)點，先介紹SURF方法的步驟和基本原理，隨后提出結(jié)合角點檢測與SURF描述子的圖像檢測方法，并對其進行適應(yīng)于標版廣告圖標和視頻檢測的改進，發(fā)展出一套檢測視頻中廣告圖標的方法，并將其用于視頻檢測；而后設(shè)計視頻檢測軟件，并在實驗中驗證其有效性。據(jù)此，本文主要包括以下內(nèi)容： 1、從圖像間的匹配與比較的角度出發(fā)，并考慮到圖像間可能存在的尺寸、旋轉(zhuǎn)、視角、噪聲變化等變化因素，選用對上述變化均有適應(yīng)性和魯棒性的SURF算法進行研究，介紹SURF算法基本步驟與原理。 2、出于快速性考慮，從SURF方法出發(fā)，提出結(jié)合角點檢測與SURF描述子的圖像檢測方法。在簡單介紹各類角點檢測方法后，將角點檢測與計算SURF描述子結(jié)合，形成新的圖像檢測方法，在保持快速性的前提下賦予角點尺度信息，解決圖像匹配的尺度變化適應(yīng)性問題，使用實際廣告圖標例子對圖像檢測方法進行實例驗證，并與傳統(tǒng)SURF方法比較，分析其優(yōu)越性。 3、由于圖像檢測方法在標版類廣告圖標的匹配工作中仍不甚理想，本文根據(jù)廣告圖標的特殊性對圖像檢測方法進行匹配比計算和多模板圖像匹配算法等多方面改進，以更適用于對視頻中標版類廣告圖標的檢測工作。 4、在對方法進行適應(yīng)于廣告圖標的若干改進后，需將方法應(yīng)用于實際的視頻檢測工作中。本文在此先介紹視頻的結(jié)構(gòu)，并在此基礎(chǔ)上針對視頻的特殊性對方法加以改進，提出了算法用于視頻的具體方法，以達到檢測視頻中標版類廣告圖標的目的，并分析方法的有效性。 5、最后依照軟件工程的相關(guān)理論，使用基于數(shù)據(jù)流的方法設(shè)計視頻檢測軟件。并通過軟件操作的演示，使用實例驗證，，以最終達到準確、實時地檢測視頻中廣告圖標的目的。
[Abstract]:With the acceleration of commercialization, the elements of advertising in the field of modern media are increasing day by day, and the development of media industry has also put forward new and higher requirements for video monitoring. As the regulator of the media market, the video monitoring department often needs to detect the location and length of the appearance of an advertising icon in the video, in order to standardize the business behavior of each advertisers. Therefore, the related technology has become a hot topic in the field of media, and it is also a necessary guarantee for the healthy development of the media industry. Because the related technologies involve image processing, image understanding and other emerging fields, lack of existing methods and theoretical basis, so the research on the above technologies is also quite difficult. For example, the display of advertising icons in video is often accompanied by changes in size, rotation, lighting and so on. If the pixel-based comparison method or content-based image detection method is used, it can not adapt to such a variety of changes. In addition, as a sequence of images on the timeline, video increases the difficulty of detection after increasing the time dimension. The lack of methods and theories not only hinders the research in related fields, but also hinders the development of related industries. Based on the theory and starting point of SURF method, this paper first introduces the steps and basic principles of SURF method, and then proposes an image detection method combining corner detection and SURF descriptive. It is suitable for the improvement of standard advertisement icon and video detection, and develops a set of methods to detect the advertisement icon in video, and applies it to video detection. Then the video detection software is designed and verified in the experiment. According to this, this paper mainly includes the following contents: 1, from the point of view of image matching and comparison, and taking into account the possible size, rotation, angle of view, noise change and other factors between images, The SURF algorithm, which has adaptability and robustness, is selected to study the above changes, and the basic steps and principles of SURF algorithm are introduced. 2. For the sake of rapidity, an image detection method combining corner detection and SURF descriptive is proposed based on SURF method. After briefly introducing all kinds of corner detection methods, a new image detection method is formed by combining corner detection with computational SURF description. Under the premise of maintaining rapidity, corner scale information is given to solve the adaptability of image matching to scale change. The actual advertising icon example is used to verify the image detection method, and compared with the traditional SURF method, its advantages are analyzed. 3. Because the image detection method is still not ideal in the matching of typesetting advertising icons, this paper improves the image detection methods in many aspects, such as matching ratio calculation and multi-template image matching algorithm according to the particularity of advertising icons. It is more suitable for the detection of video winning advertising icons. 4. After some improvements of the method to adapt to the advertisement icon, the method should be applied to the actual video detection work. In this paper, the structure of video is introduced, and on this basis, the method is improved according to the particularity of video, and the concrete method of using algorithm for video is put forward in order to detect the advertisement icon of the winning version of video. The effectiveness of the method is analyzed. Finally, according to the related theories of software engineering, the video detection software is designed by using the method based on data flow. Through the demonstration of software operation, an example is used to verify that the advertising icon in video can be detected accurately and in real time.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.41
本文編號：2481711
[Abstract]:With the acceleration of commercialization, the elements of advertising in the field of modern media are increasing day by day, and the development of media industry has also put forward new and higher requirements for video monitoring. As the regulator of the media market, the video monitoring department often needs to detect the location and length of the appearance of an advertising icon in the video, in order to standardize the business behavior of each advertisers. Therefore, the related technology has become a hot topic in the field of media, and it is also a necessary guarantee for the healthy development of the media industry. Because the related technologies involve image processing, image understanding and other emerging fields, lack of existing methods and theoretical basis, so the research on the above technologies is also quite difficult. For example, the display of advertising icons in video is often accompanied by changes in size, rotation, lighting and so on. If the pixel-based comparison method or content-based image detection method is used, it can not adapt to such a variety of changes. In addition, as a sequence of images on the timeline, video increases the difficulty of detection after increasing the time dimension. The lack of methods and theories not only hinders the research in related fields, but also hinders the development of related industries. Based on the theory and starting point of SURF method, this paper first introduces the steps and basic principles of SURF method, and then proposes an image detection method combining corner detection and SURF descriptive. It is suitable for the improvement of standard advertisement icon and video detection, and develops a set of methods to detect the advertisement icon in video, and applies it to video detection. Then the video detection software is designed and verified in the experiment. According to this, this paper mainly includes the following contents: 1, from the point of view of image matching and comparison, and taking into account the possible size, rotation, angle of view, noise change and other factors between images, The SURF algorithm, which has adaptability and robustness, is selected to study the above changes, and the basic steps and principles of SURF algorithm are introduced. 2. For the sake of rapidity, an image detection method combining corner detection and SURF descriptive is proposed based on SURF method. After briefly introducing all kinds of corner detection methods, a new image detection method is formed by combining corner detection with computational SURF description. Under the premise of maintaining rapidity, corner scale information is given to solve the adaptability of image matching to scale change. The actual advertising icon example is used to verify the image detection method, and compared with the traditional SURF method, its advantages are analyzed. 3. Because the image detection method is still not ideal in the matching of typesetting advertising icons, this paper improves the image detection methods in many aspects, such as matching ratio calculation and multi-template image matching algorithm according to the particularity of advertising icons. It is more suitable for the detection of video winning advertising icons. 4. After some improvements of the method to adapt to the advertisement icon, the method should be applied to the actual video detection work. In this paper, the structure of video is introduced, and on this basis, the method is improved according to the particularity of video, and the concrete method of using algorithm for video is put forward in order to detect the advertisement icon of the winning version of video. The effectiveness of the method is analyzed. Finally, according to the related theories of software engineering, the video detection software is designed by using the method based on data flow. Through the demonstration of software operation, an example is used to verify that the advertising icon in video can be detected accurately and in real time.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.41
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