The week's pick
Reseach Article
Automated Referee Whistle Sound Detection for Extraction of Highlights from Sports Video
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 11 |
| Year of Publication: 2011 |
| Authors: P. Kathirvel, Dr. M. Sabarimalai Manikandan, Dr. K. P. Soman |
10.5120/1729-2340
|
P. Kathirvel, Dr. M. Sabarimalai Manikandan, Dr. K. P. Soman . Automated Referee Whistle Sound Detection for Extraction of Highlights from Sports Video. International Journal of Computer Applications. 12, 11 ( January 2011), 16-21. DOI=10.5120/1729-2340
Abstract
This paper proposes a simple and automated referee whistle sound detection (RWSD) for sports highlights extraction and video summarization. The proposed method is based on preprocessor, linear phase bandpass finite impulse response (FIR) filter short-time energy estimator and decision logic. At the processing stage the discrete audio sequence is divided into non-overlapping blocks and then amplitude normalization is performed. Then, a bandpass filter is designed to accentuate referee whistle sound and suppress other audio events. Then, the filtered signal is fed to short-time energy (STE) estimator which includes amplitude squarer and linear filter to obtain a positive signal. In this work, we use decision rules based on the amplitude-dependent threshold and time-dependent threshold for detecting of referee whistle sound regions. The performance of the proposed design is tested using a large scale audio database including American football, soccer, and basket ball. The total duration of the test audio signal is approximately 12 hours and 11 minutes. The proposed method results in time-instants of boundaries of whistle sounds and then time instants are used to automatically extract the sports highlights from the unscripted video. Then, audio perception of the extracted sound segments is performed to indentify the false positive (FP) and false negative (FN). The proposed method has a detection failure rate of 19.4% (42 FP and 26 FN) and detects 324 whistle sounds successfully. The sensitivity and reliability of the proposed design are 92.5% and 80.5%, respectively. The design is implemented in MATLAB 7.0 version environment with the following system specifications: Intel (R) Pentium (R) Dual Quad CPU @ 2.40 GHz and 3 GB of RAM. The computation time is approximately 0.3-second for processing of 1-second block.
References
- C. Xu, J. Wang, H. Lu, and Y. Zhang, “A novel framework for semantic annotation and personalized retrieval of sports video,” IEEE Trans. on Multimedia, vol. 10, No. 3, pp. 421-436, April 2008.
- Z. Xiong, R. Radhakrishnan, A. Divakaran, and T.S. Huang, “Audio events detection based highlights extraction from baseball, golf and soccer games in a unified framework,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 5, pp. 632 – 635, 2003.
- P. Chang, M. Han, and Y. Gong, “Extract highlights from baseball game video with hidden Markov models,” in Proceedings of International Conference on Image Processing (ICIP), vol. 1, pp. 609–612, 2002.
- Y. Rui, A. Gupta, and A. Acero, “Automatically extracting highlights for TV baseball programs,” Eighth ACM International Conference on Multimedia, pp. 105 –115, 2000.
- R. Cai, L. Lu, A. Hanjalic, H.-J. Zhang, and L.-H. Cai, “A flexible framework for key audio effects detection and auditory context inference,” IEEE Trans. Audio, Speech, Lang, Process, vol. 14, no. 3, pp.1026–1039, May 2006.
- A. Hanjalic, “Generic approach to highlight detection in a sport video,” in Proceedings of IEEE International Conference on Image Processing (ICIP), vol. 1, pp. 1–4, 2003.
- X. F. Tong, H. Q. Lu, Q. S. Liu, and H. L. Jin, “Replay detection in broadcasting sports video,” in Proceedings of 3rd International Conference on Image and Graphics, pp. 337-340, 2004.
- I.Otsuka, R. Radharkishnan, M. Siracusa, A. Divakaran, and H. Mishima, “An enhanced video summarization system using audio features for a personal video recorder,” IEEE Trans. Consumer Electron., vol. 52, no. 1, pp. 168–172, Feb. 2006.
- Ekin, A. M. Tekalp, and R. Mehrotra, “Automatic soccer video analysis and summarization,” IEEE Trans. Image Processing, vol. 12, no. 7, 2003.
- N. Babaguchi, Y. Kawai, and T. Kitahasgi, “Event based indexing of broadcasted sports video by intermodal collaboration,” IEEE Trans. Multimedia, vol. 4, no. 1, pp. 68–75, 2002.
- H. Pan, B. Li, and M. Sezan, “Automatic detection of replay segments in broadcast sports programs by detection of logos in scene transitions,” in Proc. IEEE ICASSP, 2002.
- D. Zhang and S. F. Chang, “Event detection in baseball video using superimposed caption recognition,” in Proc. ACM Multimedia, pp. 315–318.
- R. Cai, L. Lu, H.-J. Zhang, and L.-H. Cai, “Highlight sound effects detection in audio stream,” in Proc. IEEE ICM., 2003, vol. 3, pp. 37–40.
- R. Jarina, J. Olajec, “Discriminative feature selection for applause sounds detection,” in Proc. 8th Int. Workshop on Image Analysis for Multimedia Interactive Service , Greece, 6–8 June 2007, pp. 13–16.
- M. Xu, L. Duan, C. Xu, M. Kankanhalli, and Q. Tian, “Event detection in basketball video using multi-modalities,” in Proc. IEEE Pacific Rim Conf. Multimedia, Singapore, Dec. 15–18, vol. 3, pp. 1526–1530, 2003.
Index Terms
Keywords