Multimedia Tools and Applications

, Volume 60, Issue 1, pp 233–255 | Cite as

HMM based soccer video event detection using enhanced mid-level semantic

  • Xueming Qian
  • Huan Wang
  • Guizhong Liu
  • Xingsong Hou


Highlight detection is a fundamental step in semantics based video retrieval and personalized sports video browsing. In this paper, an effective hidden Markov models (HMMs) based soccer video event detection method based on a hierarchical video analysis framework is proposed. Soccer video shots are classified into four coarse mid-level semantics: global, median, close-up and audience. Global and local motion information is utilized for the refinement of coarse mid-level semantics. Sequential soccer video is segmented into event clips. Both the temporal transitions of the mid-level semantics and the overall features of an event clip are fused using HMMs to determine the type of event. Highlight detection performance of dynamic Bayesian networks (DBN), conditional random fields (CRF) and the proposed HMM based approach are compared. The average F-score of our highlights (including goal, shoot, foul and placed kick) detection approach is 82.92%, which outperforms that of DBN and CRF by 9.85% and 11.12% respectively. The effects of number of hidden states, overall features, and the refinement of mid-level semantics on the event detection performance are also discussed.


Hidden Markov model Highlight Event detection Shot classification Soccer video 



This work is supported by the National Natural Science Foundation of China No.60903121, Chinese Center University Foundation XJTU-HRT-002, and Microsoft Research Foundation FY11-RES-THEME-052. The authors give their special thanks to Wenjun Zeng with the Computer Science Department of University of Missouri for proof reading the paper and discussion.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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