臺灣博碩士論文加值系統

在本篇論文中，我們提出一簡單的架構，僅使用少數的場景特徵如顏色及動量特徵有效率地分析MPEG-2棒球影片。我們的目標是偵測出大部分的重要事件並提供多階層式摘要。首先偵測投球場景，並將影片切割成一連串以投球場景為始的片段(投球語意單元，PSU)。接著利用主客場球衣顏色不同的特性，分析投手球衣顏色資訊找出換場事件(攻方、守方交替事件)，將影片半局為單位結構化。另外分析投球語意單元特徵，選出較長，較動態，要與球場上事件相關的內容當作重要事件。最後我們為每個選出的重要事件算分數以達成多層次摘要架構。在影片分析之後，我們能夠提供棒球影片的索引及多層次摘要內容，幫助使用者快速了解影片內容及增加影片的應用。實驗結果證實，我們方法是簡單有效的。

In this thesis, we propose a simple framework only using a few cinematic features, such as color features and motion features to analyze the MPEG-2 baseball video efficiently. Our objective is to detect most of the highlight events, and supply multi-level summaries. We first detect the pitching shots, and divide the baseball video into a sequence of segments starting with a pitching shot (Pitching Semantic Units, PSU). Then we use the property of different uniform colors between home team and visit team to find out the change events (an exchange of offensive and defensive). Moreover, we analyze the PSU information to take events that are longer, more active, and more relevant to in-filed content as the highlight events. Finally, we compute a score for each highlight event to implement the multi-level summarization framework. After the analysis process, we can provide the indices and the multi-level summaries of the baseball video to help user to comprehend the video content quickly and extend the applications of the video. Experimental results indicate that the proposed method is simple and effective.

Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Sports Video Analysis Methods 2
1.3 Organization of This Thesis 4
Chapter 2 Background Knowledge 5
2.1 Color models 5
2.2 Overview of MPEG-2 Standard 7
2.3 Shot Change Detection Method 12
2.4 Overview of MPEG-7 Multimedia Description Schemes 14
Chapter 3 The Proposed Method 17
3.1 The Field Dominant Color Detection Method 17
3.2 Shot Classification and Pitching Shot Detection 22
3.2.1 Shot Classification 22
3.2.2 Pitching Shot Detection 27
3.3 Event Detection and Summarization 29
3.3.1 Change Event Detection 30
3.3.2 Highlight Event Detection 32
3.3.3 Multi-level Summarization 34
Chapter 4 Experimental Results 36
4.1 Experimental Environment and Test Data 36
4.2 Experimental Results 38
Chapter 5 Conclusions and Future Works 45
References 47

References

[1] N. Day and J. M. Martinez, “Overview of the MPEG-7 Standard (version 4.0),” ISO/IEC JTC1/SC29/WG11 N4675 Jeju, March 2002
[2] A. Ekin, A. M. Tekalp, and Rajiv Mehrotra “Automatic Soccer Video Analysis and Summarization,” IEEE Trans. Image Processing, Vol. 12, No. 7, pp. 796-807, July 2003
[3] D. Zhong and S. F. Chang, “Structure Analysis of Sports Video Using Domain Models,” IEEE International Conference on Multimedia and Expo, pp. 22-25, August 2001
[4] S. F. Chang, D. Zhong and R. Kumar, “Real-Time Content-Based Adaptive Streaming of Sports Videos,” Content-Based Access of Image and Video Libraries, 2001 (CBAIVL 2001) IEEE Workshop on, Dec 2001
[5] T. Kawashima, K. Tateyama, T. Iijima and Y. Aoki, “Indexing of Baseball Telecast for Content-based Video Retrieval,” IEEE International Conference on Image Processing, vol.1, pp. 871-874, Oct 1998
[6] D. Zhang and S. F. Chang, “Event Detection in Baseball Video Using Superimposed Caption Recognition,” 10th ACM International Conference on Multimedia, pp. 315-318, 2002
[7] C. L. Huang and C. Y. Chang, “Video summarization using Hidden Markov Model,” IEEE International Conference on Information Technology: Coding and Computing, pp. 473-477, 2001
[8] H. C. Shih and C. L. Huang, “Image Analysis and Interpretation for Semantics Categorization in Baseball Video,” IEEE International Conference on Information Technology: Coding and Computing [Computers and Communications], pp. 379-383, 2003
[9] W. Hua, M. Han and Y. Gong, “Baseball Scene Classification Using Multimedia Features,” IEEE 2002
[10] P. Chang, M. Han and Y. Gong, “Extract Highlights From Baseball Game Video with Hidden Markov Models” IEEE International Conference on Multimedia and Expo, Vol. 1, pp. 821-824, Aug. 2002
[11] M. Han, W. Hua, W. Xu and Y. Gong, “An integrated Baseball Digest System Using Maximum Entropy Method,” ACM international conference on Multimedia, pp. 347-350, 2002
[12] R. C. Gonzales and R. E. Woods “Digital Image Processing,” Prentice Hall, 2002
[13] D. S. Taubman, M. W. Marcellin, “JPEG2000: image compression fundamentals, standards, and practice,” Kluwer Academic Publishers, 2002
[14] ISO/IEC IS 13818-2, MPEG-2 Video
[15] F. Idris and S. Panchanathn, “Review of Image and Video Indexing Technique”, Journal of Visual Communication and Image Representation, Vol. 8, No. 2, pp. 146-166 1997
[16] I. Koprinska and S. Carrato, “Temporal Video Segmentation: A Survey,” Signal Processing: Image Communication, Vol. 16, pp. 477-500, 2001
[17] W. T. Lee, “MPEG Video Analysis – Shot Change Detection and Classification,” Master Thesis, Institute of Computer and Information Science, National Chaio Tung University, 2003
[18] V. Kobla, D. Doermann, K. I. Lin, and C. Faloutsos, “Compressed domain video indexing techniques using DCT and Motion Vector information in MPEG video,” in Proc. of the SPIE Conference on Storage and Retrieval for Still Image and Video Databases V, vol. 3022, pp200-211, 1997
[19] J. B. McQueen, “Some methods of classification and analysis of multivariate observations,” In Proc. of 5th Berkeley Symp. on Mathematical Statistics and Probability, pp. 281-297, 1967
[20] S.C. Pei and Y. Z. Chou, “Efficient MPEG Compressed Video Analysis Using the Macroblock Type Information,” IEEE Trans. Multimedia, Vol. 1 No. 4, PP. 321-333, 1999
[21] X.D. Zhang, T. Y. Liu, K. T. Lo, and J. Feng, “Dynamic Selection and Effective Compression of Key Frames for video abstraction,” Pattern Recognition Letters, Vol. 24, pp. 1523-1532, 2003
[22] Z. H. Liu, “A Content Retrieval System Based on MPEG-7 Descriptors and JPEG2000 for Mobile Applications,” Master Thesis, Institute of Computer and Information Science, National Chaio Tung University, 2003