Understanding Sports Video Using Players Trajectories - Intelligent Video Event Analysis and Understanding - page 152

Finally, perspectives of extension to “general” high level analysis of videos may
also be reachable, especially by extending the proposed scheme for activity-based
processing of sports videos to video surveillance (for example, crowd event analy-
sis, steal detection, dropping off suspect object recognition...). The use of scenario
definition tools (for example, the logical grammars recently investigated in [24])
would be beneficial, providing ways to create complex and extended scenario into a
statistical framework such that the one presented in this work.
References
1. Anjum, N., Cavallaro, A.: Multifeature object trajectory clustering for video analysis.
IEEE Trans. on Circuit and Systems for Video Technology, Special issue on event anal-
ysis in videos 18(11), 1555-1564 (2008)
2. http://gforge.inria.fr/projects/audioseg
3. http://vision.fe.uni-lj.si/cvbase06/download/dataset.html
4. http://vision.fe.uni-lj.si/cvbase06/download/
CVBASE-06manual.pdf
5. http://www.dailymotion.com/
6. Ge, X.: Segmental semi-Markov models and applications to sequence analysis. Ph. D.
thesis. University of California (2002)
7. G unsel, B., Tekalp, A.M., van Beek, P.J.L.: Content-based access to video objects:
temporal segmentation, visual summarization, and feature extraction. Signal Process-
ing 66(2), 261-280 (1998)
8. Hervieu, A., Bouthemy, P., Le Cadre, J.-P.: A HMM-based method for recognizing dy-
namic video contents from trajectories. In: Proc. of the IEEE International Conference
on Image Processing (2007)
9. Hervieu, A., Bouthemy, P., Le Cadre, J.-P.: A statistical video content recognition method
using invariant features on object trajectories. IEEE Trans. on Circuit and Systems for
Video Technology, Special issue on event analysis in videos 18(11), 1533-1543 (2008)
10. Hervieu, A., Bouthemy, P., Le Cadre, J.-P.: Trajectory-based handball video understand-
ing. In: Proc. of the ACM Conference on Image and Video Retrieval (2009)
11. Hongeng, S., Nevatia, R., Bremond, F.: Large-scale event detection using semi-hidden
Markov models. In: Proc. of the IEEE International Conference on Computer Vision
(2003)
12. Jung, C.R., Hennemann, L., Musse, S.R.: Event detection using trajectory clustering and
4-D histograms. IEEE Trans. on Circuit and Systems for Video Technology, Special issue
on event analysis in videos 18(11), 1565-1574 (2008)
13. Kokaram, A., Rea, N., Dahyot, R., Tekalp, M., Bouthemy, P., Gros, P., Sezan, I.: Brows-
ing sports video (Trends in sports-related indexing and retrieval work). IEEE Signal Pro-
cessing Magazine 23(2), 47-58 (2006)
14. Kristan, M., Pers, J., Perse, M., Bon, M., Kovacic, S.: Multiple interacting targets track-
ing with application to team sports. In: Proc. off the International Symposium on Image
and Signal Processing and Analysis (2005)
15. Mermelstein, P.: Distance measures for speech recognition, psychological and instru-
mental. In: Proc. of the Workshop on Pattern Recognition and Artificial Intelligence
(1976)
16. Muscariello, A., Gravier, G., Bimbot, F.: Variability tolerant audio motif discovery. In:
Proc. of the International Conference on Multimedia Modeling (2009)