HMM based structuring of tennis videos using visual and audio cues

Authors:
E. Kijak;G. Gravier;P. Gros;L. Oisel;F. Bimbot
Affiliations:
Thomson Multimedia R& D, Cesson Sevigne, France;Nat. Inst. of Informatics, Tokyo, Japan;Nat. Inst. of Informatics, Tokyo, Japan;Nat. Inst. of Informatics, Tokyo, Japan;Perceptual Interfaces & Reality Lab., Maryland Univ., College Park, MD, USA
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Year:
2003

Citing 0
Cited 10

Player action recognition in broadcast tennis video with applications to semantic analysis of sports game

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Parametric model for video content analysis

Pattern Recognition Letters
An event detection framework in video sequences based on hierarchic event structure perception

ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
Semantic concept extraction from sports video for highlight generation

MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Accumulated motion energy fields estimation and representation for semantic event detection

CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Semantic concept mining in cricket videos for automated highlight generation

Multimedia Tools and Applications
Hierarchical decision making scheme for sports video categorisation with temporal post-processing

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Semantic event detection in structured video using hybrid HMM/SVM

CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A hierarchical framework for generic sports video classification

ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Hierarchical Hidden Markov Model in detecting activities of daily living in wearable videos for studies of dementia

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper focuses on the use of hidden Markov models (HMMs) for structure analysis of videos, and demonstrates how they can be efficiently applied to merge audio and visual cues. Our approach is validated in the particular domain of tennis videos. The basic temporal unit is the video shot. Visual features describe the audio events within a video shot. The video structure parsing relies on the analysis of the temporal interleaving of video shots, with respect to prior information about tennis content and editing rules. As a result, typical tennis scenes are identified. In addition, each shot is assigned to a level in the hierarchy described in terms of point, game and set.