A framework for flexible summarization of racquet sports video using multiple modalities
Computer Vision and Image Understanding
Highlight Ranking for Broadcast Tennis Video Based on Multi-modality Analysis and Relevance Feedback
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Player Detection and Tracking in Broadcast Tennis Video
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
An intelligent strategy for the automatic detection of highlights in tennis video recordings
Expert Systems with Applications: An International Journal
Video Shrinking by Auditory and Visual Cues
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Personalized online video recommendation by neighborhood score propagation based global ranking
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Semantic concept mining in cricket videos for automated highlight generation
Multimedia Tools and Applications
Multimedia Tools and Applications
Tennis Video 2.0: A new presentation of sports videos with content separation and rendering
Journal of Visual Communication and Image Representation
Bayesian belief network based broadcast sports video indexing
Multimedia Tools and Applications
Journal of Visual Communication and Image Representation
Recognizing tactic patterns in broadcast basketball video using player trajectory
Journal of Visual Communication and Image Representation
Multimedia Tools and Applications
Sports Information Retrieval for Video Annotation
International Journal of Digital Library Systems
Recognizing jump patterns with physics-based validation in human moving trajectory
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
The majority of existing work on sports video analysis concentrates on highlight extraction. Little work focuses on the important issue as how the extracted highlights should be organized. In this paper, we present a multimodal approach to organize the highlights extracted from racket sports video grounded on human behavior analysis using a nonlinear affective ranking model. Two research challenges of highlight ranking are addressed, namely affective feature extraction and ranking model construction. The basic principle of affective feature extraction in our work is to extract sensitive features which can stimulate user's emotion. Since the users pay most attention to player behavior and audience response in racket sport highlights, we extract affective features from player behavior including action and trajectory, and game-specific audio keywords. We propose a novel motion analysis method to recognize the player actions. We employ support vector regression to construct the nonlinear highlight ranking model from affective features. A new subjective evaluation criterion is proposed to guide the model construction. To evaluate the performance of the proposed approaches, we have tested them on more than ten-hour broadcast tennis and badminton videos. The experimental results demonstrate that our action recognition approach significantly outperforms the existing appearance-based method. Moreover, our user study shows that the affective highlight ranking approach is effective.