隨著多媒體內容在網路上的普及。近幾年這趨勢引發了虛擬廣告技術的誕生，尤其是針對能帶來巨大商機的運動影片。虛擬廣告的想法是希望將品牌商標和其他廣告訊息置入影片，使觀眾可以專注在觀看比賽的同時又會不自覺的對廣告產生印象。然而，利用人工方式來做這項工作是很費時且沒有效率的。基於上述理由，在這篇論文中，我們提出了一個全方面性的虛擬廣告置入方法用在網球影片上，希望提升廣告的有效性同時又不會增加觀眾的視覺干擾。在廣告置入時間方面，我們根據網球視頻結構分析和視覺特徵來檢測適當的置入時間點。置入影片位置的選擇，我們則是基於人類的視覺系統分析，考慮空間和時間關注度來避免廣告遮蔽住影片重要部分。同時，為了增加廣告的有效性，廣告位置的重要程度也納入決定考量。接著，廣告選擇機制和色彩呈現方式用來提升用戶體驗同時也減少視覺干擾。由實驗結果證明，我們提出的自動偵測置入方式是有效且適當的。除此之外依照規定的準則來選擇廣告置入時間和位置的結果顯示和使用者測試研究也是一致的。

The community contributed media content on the Internet have become one of the primary sources. This trend triggers the virtual advertising comes up recent years, especially for sport videos which bring tremendous commercial potentials. The concept of virtual advertising is to bring brand names or other advertising messages into scenes, thus the audience can focus on the game while impressed with advertisement unconsciously. However, the task is labor intensive and inefficient by manually. In this thesis, a novel approach to build up a more comprehensive scheme which will equilibrate on promoting cognitive effectiveness but curtailing visual intrusiveness for tennis highlight videos is proposed. This approach determines appropriate insertion timing according to the structure of video sequence and the visual features gathered in each frame. The inappropriate locations are masked using the proposed attention analyzing procedure through spatial and temporal examination. In order to promote advertising effectiveness, two considerations of significant localization also take into account in the implementation. Then, an advertising selection mechanism with color relevance is implemented to reduce visual interference from the perspective of user experiences. The objective assessments demonstrate that this advertising insertion approach is both effective and adequate. In addition, the subjective evaluation results show that the insertion method with predefined criteria is accordant to user study experiments.

[1] T. Mei, X.S. Hua and S. Li, “VideoSense: A Contextual In-Video Advertising System,” IEEE Transactions on Circuits and Systems for Video Technology, vol.19, pp. 1866-1879, Dec. 2009.
[2] X.S. Hua, T. Mei and S. Li, “When Multimedia Advertising Meets the New Internet Era,” IEEE Workshop on Multimedia Signal Processing, pp.1-5, Oct. 2008.
[3] Y.F. Ma, L. Lu, H.J. Zhang and M.J. Li, “A User Attention Model for Video Summarization,” ACM International Conference on Multimedia, 2002.
[4] J. Wang, C. Xu, E. Chng and Q. Tian, “Sports Highlight Detection from Keyword Sequences Using HMM,” IEEE International Conference on Multimedia and Expo, vol.1, pp. 599 – 602, 2004.
[5] Google AdSense.[Online]. Avaliable: http://adsense.goole.com.
[6] Google AdWords.[Online] . Avaliable: http://adwords.goole.com.
[7] A. Patel, P. Patel, “Virtual Advertisement with Live Entertainment Program for Real-Time Video Application,” IEEE International Conference on Advances in Computer Engineering, pp. 319 – 322, June 2010.
[8] K. Wan, X.Yan, X.Yu and G. Xu, “Robust Goal-Mouth Detection for Virtual Content Insertion,” ACM International Conference on Multimedia, 2003.
[9] C. Xu, K.W. Wan, S.H. Bui, Q. Tian, “Implanting Virtual Advertisement into Broadcast Soccer Video,” Pacific Rim Conference on Multimedia, 2004.
[10] H. Liu, S. Jiang, Q. Huang and C. Xu, “A Generic Virtual Content Insertion System Based on Visual Attention Analysis,” ACM International Conference on Multimedia, 2008.
[11] H. Liu, S. Jiang, Q. Huang, C. Xu, “Lower attentive region detection for virtual content insertion in broadcast video,” IEEE International Conference on Multimedia and Expo, pp.1529–1532, 2008
[12] C.H. Chang, K.Y. Hsieh, M.C. Chung, and J.L. Wu, “ViSA: virtual spotlighted advertising,” ACM International Conference on Multimedia, 2008.

[13] C.H. Chang, K.Y. Hsieh, M.C. Chiang and J.L. Wu, “Virtual spotlighted advertising for tennis videos,” Journal of Visual Communication and Image Representation, vol.21, pp. 595-612, 2010.
[14] H. Liu, X. Qiu, Q. Huang, S. Jiang, C. Xu, “Advertise gently-in-image advertising with low intrusiveness,” IEEE International Conference on Image Processing, pp. 3105 – 3108, Nov. 2009.
[15] T. Mei, X.S. Hua, L.J. Yang, S.P. Li, “VideoSense-Towards Effective Online Video Advertising,” ACM International Conference on Multimedia, Dec. 2007.
[16] S.H. Srinivasan, N. Sawant and S. Wadhwa, “vADeo:video advertising system,” ACM International Conference on Multimedia, 2007.
[17] K. Wan and C.S. Xu, “Automatic Content Placement in Sports Highlights,” IEEE International Conference on Multimedia and Expo, pp.1893-1896, July. 2006.
[18] Y. Li, K.W. Wan, X. Yan, Changsheng Xu, “Real time advertisement insertion in baseball video based on advertisement effect,” ACM International Conference on Multimedia, 2005.
[19] L. Itti, C. Koch , E. Niebur, “A Model of Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1254-1259, Nov 1998.
[20] C. Koch, S. Ullman, “Shifts in selective visual attention: Towards the underlying neural circuitry”, Human Neurobiology 4, pp.219–227, 1985.
[21] X. Hou, L. Zhang, “Saliency Detection: A Spectral Residual Approach,” IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
[22] O. Le Meur, D. Thoreau, P. Le Callet, D. Barba, “A spatio-temporal model of the selective human visual attention,” IEEE International Conference on Image Processing, pp.1188-1191, Sep. 2005.
[23] S. Xu, W. Lin, C.C.J. Kuo, “Fast Visual Saliency Map Extraction from Digital Video,” International Conference on Consumer Electronics, pp.1-2, Jan. 2009.
[24] Y.L. Tian and A. Hampapur, “Robust Salient Motion Detection with Complex Background for Real-Time Video Surveillance,” IEEE Workshop on Motion and Video Computing, pp.30-35, Jan. 2005.

[25] B. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proc. of Imaging Understanding Workshop, pp. 121-130, 1981.
[26] J. Assfalg, M. Bertini, C. Colombo, A.D. Bimbo, “Semantic annotation of sports videos,” IEEE Multimedia, vol.9, pp.52-60, 2002.
[27] J.Y. Bouguet, “Pyramidal Implementation of the Lucas Kanade Feature Tracker-Description of the algorithm,” Microprocessor Research Labs, Tech. Rep, 2001.
[28] Youtube.[Online]. Avaliable: http://www.youtube.com.
[29] H. Liu, X. Qiu, Q. Huang, S. Jiang , C. Xu, “Advertise gently-in-image advertising with low intrusiveness,” IEEE International Conference on Image Processing, pp.3105-3108, Nov. 2009.