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A new spatio-temporal method for event detection and personalized retrieval of sports video

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Abstract

In this paper, a new spatio-temporal method for adaptively detecting events based on Allen temporal algebra and external information support is presented. The temporal information is captured by presenting events as the temporal sequences using a lexicon of non-ambiguous temporal patterns. These sequences are then exploited to mine undiscovered sequences with external text information supports by using class associate rules mining technique. By modeling each pattern with linguistic part and perceptual part those work independently and connect together via transformer, it is easy to deploy this method to any new domain (e.g baseball, basketball, tennis, etc.) with a few changes in perceptual part and transformer. Thus the proposed method not only can work well in unwell structured environments but also can be able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Results of automatic event detection progress are tailored to personalized retrieval via click-and-see style using either conceptual or conceptual-visual query scheme. Experimental results carried on more than 30 hours of soccer video corpus captured at different broadcasters and conditions as well as compared with well-known related methods, demonstrated the efficiency, effectiveness, and robustness of the proposed method in both offline and online processes.

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References

  1. Allen J (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843

    Article  MATH  Google Scholar 

  2. Calic J, Campbell N, Dasiopoulou S, Kompatsiaris Y (2005) An overview of multimodal video representation for semantic analysis. In: European workshop on the integration of knowledge, semantics and digital media technologies, conference proceedings, pp 39–45

  3. Chen M, Chen S, Shyu M, Wickramaratna K (2006) Semantic event detection via multimodal data mining. IEEE Signal Process Mag 23(2):38–46

    Article  Google Scholar 

  4. Chen M, Chen S, Shyu M (2007) Hierarchical temporal association mining for video event detection in video databases. In: MDDM’06 conference proceedings. IEEE, Piscataway, pp 137–145

    Google Scholar 

  5. Duan L, Xu M, Tian Q, Xu C, Jin J (2005) A unified framework for semantic shot representation of sports video. IEEE Trans Multimedia 7(6):1066–1083

    Article  Google Scholar 

  6. Ekin A, Tekalp A, Mehrotta R (2003) Automatic soccer video analysis and summarization. IEEE Trans Image Process 12(7):796–807

    Article  Google Scholar 

  7. Fleischman M, Roy D (2007) Unsupervised content-based indexing of sports video. In: MIR’07 conference proceedings. ACM, New York, pp 87–94

    Google Scholar 

  8. Fleischman M, Decamp P, Roy D (2006) Mining temporal patterns of movement for video content classification. In: MIR’06 conference proceedings. ACM, New York, pp 183–191

    Google Scholar 

  9. Jiang S, Huang Q, Gao W (2007) Mining information of attack-defense status from soccer video based on scene analysis. In: ICME07 conference proceedings. IEEE, Piscataway, pp 1095–1098

    Google Scholar 

  10. Liu Y, Jiang S, Ye Q, Gao W, Huang Q (2005) Playfield detection using adaptive gmm and its application. In: ICASSP’05 conference proceedings. ACM, New York, pp 421–424

    Google Scholar 

  11. Missaoui R, Palenichka R (2005) Effective image and video mining: an overview of model-based approaches. In: MDM’05 conference proceedings. ACM, New York, pp 43–52

    Google Scholar 

  12. Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M (2004) Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng 16(11):1424–1440

    Article  Google Scholar 

  13. Sadlier D, O’Connor N (2005) Event detection in field sports video using audio-visual features and a support vector machine. IEEE Trans Circuits Syst Video Technol 15(10):1225–1233

    Article  Google Scholar 

  14. Sebe N, Tian Q (2007) Personalized multimedia retrieval: the new trend? In: ACM MIR 07 conference proceedings. ACM, New York, pp 229–306

    Google Scholar 

  15. Snoek C, Worring M (2005) Multimedia event-based video indexing using time intervals. IEEE Trans Multimedia 7(4):638–647

    Article  Google Scholar 

  16. Snoek C, Worring M (2005) Multimodal video indexing: a review of the state-of-the-art. J Multimedia Tools Appl 35(5):5–34

    Article  Google Scholar 

  17. Tong X, Lu H, Liu Q, Jian H (2004) Replay detection in broadcasting sports video. In: MIR’05 conference proceedings. ACM, New York, pp 337–340

    Google Scholar 

  18. Wang F, Sun L, Yang B, Yang S (2006) Fast arc detection algorithm for play field registration in soccer video mining. In: Systems, man, and cybernetics conference proceedings. IEEE, Piscataway, pp 4932–4936

    Google Scholar 

  19. Wu S, Chen Y (2007) Mining nonambiguous temporal patterns for interval-based events. IEEE Trans Knowl Data Eng 19(6):742–758

    Article  Google Scholar 

  20. Xiong Z, Zhou X, Tian Q, Rui Y, Huang T (2006) Semantic retrieval of video. IEEE Signal Process Mag 23(2):18–27

    Article  Google Scholar 

  21. Xu C, Wang J, Kwan K, Li Y, Duan L (2006) Live sports event detection based on broadcast video and web-casting text. In: MM’06 conference proceedings. ACM, New York, pp 221–230

    Google Scholar 

  22. Zhao Q, Bhowmick S (2003) Sequential pattern mining: a survey. ITechnical report, CAIS Nayang Technological University, Singapore, pp 1–26

  23. Zhu X, Wu X, Elmagarmid A, Feng Z, Wu L (2005) Video data mining: semantic indexing and event detection from the association perspective. IEEE Trans Knowl Data Eng 7(5):665–677

    Google Scholar 

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Correspondence to Minh-Son Dao.

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This work is partly supported by a Grant-in-Aid for scientific research from the Japan Society for the Promotion of Science (JSPS).

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Dao, MS., Babaguchi, N. A new spatio-temporal method for event detection and personalized retrieval of sports video. Multimed Tools Appl 50, 227–248 (2010). https://doi.org/10.1007/s11042-009-0379-4

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