Automatic detection of 'Goal' segments in basketball videos

ABSTRACT

Advances in the media and entertainment industries, for example streaming audio and digital TV, present new challenges for managing large audio-visual collections. Efficient and effective retrieval from large content collections forms an important component of the business models for content holders and this is driving a need for research in audio-visual search and retrieval. Current content management systems support retrieval using low-level features, such as motion, colour, texture, beat and loudness. However, low-level features often have little meaning for the human users of these systems, who much prefer to identify content using high-level semantic descriptions or concepts. This creates a gap between the system and the user that must be bridged for these systems to be used effectively. The research presented in this paper describes our approach to bridging this gap in a specific content domain, sports video. Our approach is based on a number of automatic techniques for feature detection used in combination with heuristic rules determined through manual observations of sports footage. This has led to a set of models for interesting sporting events-goal segments-that have been implemented as part of an information retrieval system. The paper also presents results comparing output of the system against manually identified goals.

REFERENCES

• 1: Myron Flickner , Harpreet Sawhney , Wayne Niblack , Jonathan Ashley , Qian Huang , Byron Dom , Monika Gorkani , Jim Hafner , Denis Lee , Dragutin Petkovic , David Steele , Peter Yanker, Query by Image and Video Content: The QBIC System, Computer, v.28 n.9, p.23-32, September 1995 [doi>10.1109/2.410146]
• 2: Shih-Fu Chang , William Chen , Horace J. Meng , Hari Sundaram , Di Zhong, VideoQ: an automated content based video search system using visual cues, Proceedings of the fifth ACM international conference on Multimedia, p.313-324, November 09-13, 1997, Seattle, Washington, United States [doi>10.1145/266180.266382]
• 3: John R. Smith , Shih-fu Chang, Querying by color regions using VisualSEEk content-based visual query system, Intelligent multimedia information retrieval, MIT Press, Cambridge, MA, 1997
• 4: Arun Hampapur and Ramesh Jain. Virage Video Engine. SPIE Vol. 3022. pp 188-197.
• 5: Atsuo Yoshitaka , Tadao Ichikawa, A Survey on Content-Based Retrieval for Multimedia Databases, IEEE Transactions on Knowledge and Data Engineering, v.11 n.1, p.81-93, January 1999 [doi>10.1109/69.755617]
• 6: Barry G. Haskell , Atul Puri , Arun N. Netravali, Digital Video: An introduction to MPEG-2, Chapman & Hall, Ltd., London, UK, 1996
• 7: Rainer Lienhart , Wolfgang Effelsberg , Ramesh Jain, VisualGREP: A Systematic Method to Compare and RetrieveVideo Sequences, Multimedia Tools and Applications, v.10 n.1, p.47-72, January 2000 [doi>10.1023/A:1009663921899]
• 8: Hari Sundaram and Shih-Fu Chang. Efficient Video Sequence Retrieval in Large Repositories. SPIE '99 Storage and Retrieval of Image and Video Databases VII, San Jose CA, Jan 24-29, 1999.
• 9: N. Bryan-Kinns, VCMF: A Framework for Video Content Modeling, Multimedia Tools and Applications, v.10 n.1, p.23-45, January 2000 [doi>10.1023/A:1009611905060]
• 10: C.Carson and V.E. Ogle. Storage and retrieval of feature data for a very large online image collection. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, I9(4):19-27, December 1996.
• 11: Brett Adams , Chitra Dorai , Svetha Venkatesh, Study of shot length and motion as contributing factors to movie tempo (poster session), Proceedings of the eighth ACM international conference on Multimedia, p.353-355, October 2000, Marina del Rey, California, United States [doi>10.1145/354384.354530]
• 12: A. Yoshitaka , Y. Hosoda , M. Hirakawa , T. Ichikawa, Content-Based Retrieval of Video Data Based on Spatiotemporal Correlation of Objects, Proceedings of the IEEE International Conference on Multimedia Computing and Systems, p.208, June 28-July 01, 1998 [doi>10.1109/MMCS.1998.693642]
• 13: M. Naphade, T. Kristjansson, B. Frey, and T.S. Huang. Probabilistic multimedia objects (multijects): A novel approach to indexing and retrieval in multimedia systems. In Proceedings of the fifth IEEE International Conference on Image Processing, Volume 3, pages 536-540, Chicago, IL, Ott 1998.
• 14: Drew D. Sam-, Yap-Peng Tan, Sanjeev R. Kulkarni, and Peter J. Ramadge. Automatic analysis and Annotation of Basketball Video. In Storage and Retrieval for Image and Video Databases V, volume SPIE-3022, pages 176-187, Feb. 1997.
• 15: G. Sudhir, John CM. Lee, and Anil K. Jam. Automatic Classification of Tennis Video for High-level Content-based Retrieval. Technical Report HKUST-CS97-2, The Hong Kong University of Science and Technology, Hong Kong, August 7, 1997.
• 16: Davis Pan, A Tutorial on MPEG/Audio Compression, IEEE MultiMedia, v.2 n.2, p.60-74, June 1995 [doi>10.1109/93.388209]
• 17: Peter Nell. MPEG Digital Audio Coding. IEEE Signal Processing Magazine. Sept. 1997. Pages 59-8
• 18: Surya Nepal, Uma Srinivasan and Graham Reynolds. Semantic-Based Retrieval Model for Digital Audio/Video. CSIRO Mathematical and Information Sciences. Technical Report No 2000/174. October 2000.
• 19: Mediaware solutions. http://www.mediaware.com.au/
• 20: MPEGMaaate: http://www.cmis.csiro.au/dmis/Maaate/
• 21: Lifang Gu. Scene Analysis of Video Sequences in the MPEG Domain. Proceedings of the IASTED International Conference Signal and Image Processing October 28-31, Las Vegas, Nevada, U.S.A.
• 22: Hongjiang Zhang , Chien Yong Low , Stephen W. Smoliar, Video parsing and browsing using compressed data, Multimedia Tools and Applications, v.1 n.1, p.89-111, March 1995 [doi>10.1007/BF01261227]
• 23: Yong Rui , Anoop Gupta , Alex Acero, Automatically extracting highlights for TV Baseball programs, Proceedings of the eighth ACM international conference on Multimedia, p.105-115, October 2000, Marina del Rey, California, United States [doi>10.1145/354384.354443]
• 24: Basketball Rules http:Nwww.basketball.coml
• 25: Uma Srinivasan, Jordi Robert-Ribes and Graham Reynolds. Querying Video Content Using Multi-Modal Features. CSIRO Mathematical and Information Sciences. Technical Report 1997.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Audio input/output

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.6 Segmentation
          Subjects: Edge and feature detection

General Terms:
Design, Experimentation, Performance

Keywords:
content-based retrieval, sports video analysis, temporal models