Project: Sports Video Analysis

Related Work

The topic we are interested in is more or less related to a rather broad area of multimedia processing and analysis. So instead of trying to give a comprehensive list in the literature, we hereby present four sub topics with closely related approach or of specific interest.
Content-based search and retrieval

Content-based search and retrieval is a comprehensive topic. Its main focuses include extraction, representation and matching of spatial-temporal features; event detection and classification based on low-level features and domain knowledge; segmentation and abstraction at multiple levels (shot, scene, topic), and relevance feedback for user interaction.  The following include a few recent surveys in this field.

[1]Shih-Fu Chang, Qian Huang , Thomas Huang, Atul Puri, and Behzad Shahraray, Multimedia Search and Retrieval, Book chapter, in Advances in Multimedia: Systems, Standards, and Networks, A. Puri and T. Chen (eds.). New York: Marcel Dekker, 1999.

[2]Smoliar, S.W.; HongJiang Zhang, Content based video indexing and retrieval, IEEE Multimedia , Volume: 1 Issue: 2 , Summer 1994, Page(s): 62 -72

[3]S.-F. Chang, J. R. Smith, M. Beigi, and A. B. Benitez, Visual Information Retrieval from Large Distributed On-line Repositories, Communications of the ACM, Vol. 40, No. 12, pp.63-71, Dec 1997.

Sports video analysis

Sports video has inherent structure constraint as defined in rules of the game and field production. And we believe this structure makes it easier to explore the correlation and interaction of domain constraints, low-level features, and high-level semantics.
Prior works include domain-specific scene classification[4], classification of audio track into excited/unexcited commentary[6], audio event spotting via template matching[5][6], interactive browsing via object tracking[7], slow-motion detection by still-frame identification in MPEG stream[9], and incorporation of field model and object tracking[8].

[4]Yihong Gong; Lim Teck Sin; Chua Hock Chuan; Hongjiang Zhang; Masao Sakauchi,Automatic parsing of TV soccer programs, Multimedia Computing and Systems, 1995., Proceedings of the International Conference on , 1995, Page(s): 167 -174

[5]Yuh-Lin Chang; Wenjun Zeng; Kamel, I.; Alonso, R., Integrated image and speech analysis for content-based video indexing, Multimedia Computing and Systems, 1996., Proceedings of the Third IEEE International Conference on , 1996, Page(s): 306 -313

[6]Yong Rui; Anoop Gupta; Alex Acero; Automatically Extracting Highlights for TV Baseball Programs, Proc. ACM Multimedia, Oct. 2000, Los Angeles USA, Pages 105 -115

[7]David Rees, Johnson I Agbinya, Nick Stone, Fu Chen, CLICK-IT: Interactive Television Highlighter for Sports Action Replay, Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on , Volume: 2 , 1998
Page(s): 1484 -1487 vol.2

[8]Sudhir, G.; Lee, J.C.M.; Jain, A.K., Automatic classification of tennis video for high-level content-based retrieval, Content-Based Access of Image and Video Database, 1998. Proceedings., 1998 IEEE International Workshop on , 1998, Page(s): 81 -90

[9]Kobla, V.; DeMenthon, D.; Doermann, D., Detection of slow-motion replay sequences for identifying sports videos, Multimedia Signal Processing, 1999 IEEE 3rd Workshop on , 1999, Page(s): 135 -140

[10]        Zhong, D; Chang, S.-F., Structure Analysis of Sports Video Using Domain Models, IEEE ICME 2001, Aug. 2001, Tokyo, Japan

[11]        V. Tovinkere , R. J. Qian, “Detecting Semantic Events in Soccer Games: Towards A Complete Solution”, Proc. ICME 2001, Tokyo, Japan, Aug 22-25, 2001

[12]        D. Yow, B.L.Yeo, M. Yeung, and G. Liu, "Analysis and Presentation of Soccer Highlights from Digital Video" Proc. ACCV, 1995, Singapore, Dec. 5-8, 1995

Probabilistic content analysis

Probabilistic reasoning enable inference based on computable audio-visual features and domain-specific knowledges. Related works include using Beyesian network to identify multimedia objects and infer multimedia concepts[11, 12], and exploiting dynamic programming techniques or hidden Markov model to distinguish different types of TV program[13].

[13]        L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition”, Proceedings of the IEEE, v 77 no 2 , P 257 –286, Feb. 1989

[14]         Seungyup Paek and Shih-Fu Chang, The Case for Image Classification Systems Based on     Probabilistic Reasoning, IEEE International Conference on Multimedia and Expo. July 30 - August 2, 2000. New York City, NY, USA.

[15]        Naphade, M.R.; Huang, T.S., A probabilistic framework for semantic video indexing, filtering, and retrieval, Multimedia, IEEE Transactions on , Volume: 3 Issue: 1 , March 2001, Page(s): 141 -151

[16]        Jincheng Huang; Zhu Liu; Yao Wang, Joint video scene segmentation and classification based on hidden Markov model, Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on , Volume: 3 , 2000, Page(s): 1551 -1554 vol.3

Front End

Effective and accurate segmentation and feature extraction is crucial to the performance of the whole content analysis system. Temporal segmentation output usually include shots, scenes, or other meaningful units. Video data can be further processed to segment video into regions or objects from which useful spatio-temporal features (such as trajectory, motion) can be extracted.

[17]         Di Zhong; Shih-Fu Chang, An integrated approach for content-based video object segmentation and retrieval, Circuits and Systems for Video Technology, IEEE Transactions on , Volume: 9 Issue: 8 , Dec. 1999, Page(s): 1259 -1268

[18]        J. Meng and S.-F. Chang, Tools for Compressed-Domain Video Indexing and Editing, Proceedings, IS&T/SPIE Symposium on Electronic Imaging: Science and Technology (EI'96) -Storage & Retrieval for Image and Video Databases IV, Vol. 2670, San Jose, CA, February 1996.

[19]        Yap-Peng Tan; Saur, D.D.; Kulkami, S.R.; Ramadge, P.J., Rapid estimation of camera motion from compressed video with application to video annotation, Circuits and Systems for Video Technology, IEEE Transactions on , Volume: 10 Issue: 1 , Feb. 2000, Page(s): 133 -146

[20]        Di Zhong; Shih-Fu Chang, AMOS: an active system for MPEG-4 video object segmentation, Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on , Volume: 2 , 1998, Page(s): 647 -651 vol.2

[21]        J.S. Boreczky, and L.A. Rowe, Comparison of Video Shot Boundary Detection Techniques, Storage and Retrieval for Image and Video Databases IV, Proc. of IS&T/SPIE 1996 Int'l Symp. on Elec. Imaging: Science and Technology, San Jose, CA, February 1996

[22]        D. Zhong and S.-F. Chang, Video Object Model and Segmentation for Content-Based Video Indexing, IEEE International Symposium on Circuits and Systems (ISCAS'97), Hong Kong, June 1997, Special Session on Networked Multimedia Technology and Application

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Last update: 11/05/2001