CONCLUSION - RESEARCH METHODOLOGY 3.1 Introduction

RESEARCH METHODOLOGY 3.1 Introduction

CHAPTER 5 CONCLUSION

CHAPTER 5

REFERENCES

[1] M Saric, H Dujmic, V Papic, N Rozic, “Player number localization and recognition in soccer video using HSV color space and internal contours,” World Academy of Science, Engineering and Technology 43 2008

[2] S Ozbay, E. Ereelebi, “Automatic Vehicle Identification by Plate Recognitio World Academy of Science, Engineering and Technology 9 2005 .

[3] I. Z. B. A. Aziz, “Object’s RGB Color Extraction”, Nov 2007, unpublished

[4] L. Ballan, M. Bertini, A. D. Bimbo, and W. Nunziati. Soccer Players Identification ` Based on Visual Local Features.

[5] E. Andrade, E. Khan, J. Woods, and M. Ghanbari. Player identification in interactive sport scenes using region space analysis prior information and number recognition.

[6] F. Chollet. Keras: Theano-based deep learning library. https://github.com/fchollet/keras, 2015.

[7] D. Delannay, N. Danhier, and C. De Vleeschouwer. Detection and recognition of sports(wo)men from multiple views.

[8] S. Gerke, S. Singh, A. Linnemann, and P. Ndjiki-Nya. Unsupervised color classifier training for soccer player detection. In Visual Communications and Image Processing (VCIP)., 2013.

[9] C.-W. Lu, C.-Y. Lin, C.-Y. Hsu, M.-F. Weng, L.-W. Kang, and H.-Y. M. Liao. Identification and Tracking of Players in Sport Videos. In Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service – ICIMCS ’13, page 113, New York, New York, USA, 2013. ACM Press.

[10] Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, number 2, page 5. Granada, Spain, 2011.

[11] Michael Isard and Andrew Blake, ―Condensation - conditional density Propagation for visual tracking,‖

IJCV, vol. 29, pp. 5–28, 1998.

[12] M. Li et al., ―Rapid and robust human detection and tracking based on omega- Shape features,‖ ICIP, 2009.

[13] Y. Zhai and Shah, ―Visual attention detection in video sequences using spatiotemporal cues,‖ ACM MM, 2006.

[14] S. Jiang, Q. Ye, W. Gao, T. Huang, ―A new method to segment playfield and its applications in match analysis in sports video,‖ ACM Multimedia, pp. 292-295, 2004.

[15] V. Vapnik, The nature of statistical learning theory, Springer-Verlag, New York, 1995.

[16] G. Zhu, D. Liang, Y. Liu, Q. Huang, W. Gao, ―Improving particle filter with support vector regression for efficient visual tracking,‖ IEEE International Conference on Image Processing, vol. 2, pp. 422-425, 2005.

[17] P. Felzenszwalb, D. McAllester, and D. Ramanan. A Discriminatively Trained, Multiscale, Deformable Part Model. In CVPR, 2008.

[18] Felzenszwalb PF, Girshick RB, McAllester D, Ramanan (2009) Object Detection with Discriminatively Trained Part Based Models.

[19] Khan SM, Shah M (2006) A Multiview Approach to Tracking People in Crowded Scenes using a Planar Homography Constraints. In: European Conference on Computer Vision.

[20] Okuma K, Taleghani A, de Freitas N, Little JJ, Lowe DG (2004) A Boosted Particl Filter: Multitarget Detection and Tracking. In: European Conference on Computer Vision .

[21] J.A. Bilmes, ―A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models,‖ TR-97- 021, Berkeley, April 1998.

[22] J.S. Liu, Monte Carlo Strategies in Scientific Computing, Springer, 2001.

[23] [23] David J. Abel and David M. Mark. A comparative analysis of some two- Dimensional orderings.

Int. Journal of Geographical Information Systems, 4:21 31, 1990. 126, 174

[24] Y.Demiris A.Dearden and O. Grau. Tracking football player movment from a single moving camera using particle liters. In The 3rd European Conf. on Visual Media Production (CVMP 2006), 2006. 38

[25] Impire AG. Vis.track. http://www.vistrack.de. [26] E.L. Andrade, E. Khan, J.C. Woods, and M. Ghanbari.

Player classification in Interactive sport scenes using prior information region space analysis and number Recognition. In Proc. of Int. Conf. on Image Processing (ICIP), volume 3, pages 129 132, 2003. 118.

[27] Andreas Andreakis, Nicolai von Hoyningen-Huene, and Michael Beetz. Incremental unsupesrvised time series analysis using merge growing neural gas. In JosØ Carlos Pr ncipe and Risto Miikkulainen, editors, Proc. of Int.Workshop on \ Advances in Self-Organizing Maps (WSOM), volume 5629 of Lecture Notes in,Computer Science, pages 10 18. Springer, 2009. 163

[28] M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp. A tutorial on particle lters fo online nonlinear/non-gaussian bayesian tracking. IEEE Tran on Signal Processing, 50(2), Feb 2002. 26, 50

[29] Franz Baader, Diego Calvanese, Deborah McGuinness, Daniele Nardi, an Peter Patel-Schneider, editors.

The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, 2 edition.

[30] Noboru Babaguchi and Ramesh Jain. Event detection from continuous media. In Proc. of Int. Conf. on Pattern Recognition (ICPR), 1998.

[31] Noboru Babaguchi, Yoshihiko Kawai, and Tadahiro Kitahashi. Event Based Video Indexing by Intermodal Collaboration. IEEE Transactions on Multimedia, 9(2):68 75, 2002.

[35] Yaakov Bar-Shalom and Thomas E. Fortmann. Tracking and Data Association,Volume 179 ofMathematics in Science and Engineering. Academic Press, 1988. 8, 26, 50, 55, 65.

[36] Yaakov Bar-Shalom, Thomas E. Fortmann, and M. Sche e. Joint probabilistic data association for multiple targets in clutter. In Proc. of Conf. on Information Sciences and Systems, Princeton, NJ, March 1980. 55, 141 [37] Yaakov Bar-Shalom and A. G. Ja er. Adaptive nonlinear altering for tracking with measurements of uncertain origin. In IEEE Conf. on Decision & Control, pages 243, 247, New Orleans, LA, December 1972. 52 [38] Yaakov Bar-Shalom and E. Tse. Tracking in a cluttered environment with probabilistic data association.

Automatic, 11:451 460, September 1975. 52

[39] Lluis Barcel , Xavier Binefa, and John R. Kender. Robust methods and representations for soccer player tracking and collision resolution. In Proc. of Int. Conf. on Image and Video Retrieval (CIVR), pages 237 246, 2005. 55

[40] Francois Bardet, Thierry Chateau, and Datta Ramadasan. Real-Time Multi-Object Tracking with Few Particles. In AlpeshKumar Ranchordas and Helder Araujo, editors, Int. Conf. on Computer Vision Theory and Applications (VISAPP), volume1, pages 456 463, Lisboa, Portugal, Feb. 2009. INSTICC, INSTICC press. 57, 78

[41] Ricardo M. L. Barros, Milton S. Misuta, Rafael P. Menezes, Pascual J. Figueroa, Felipe A. Moura, Sergio A Cunha, Ricardo Anido, and Neucimar J. Leite. Analysis of the distances covered by rest division Brazilian soccer players obtained With an automatic tracking method. Journal of Sports Science and Medicine, 6:233 242, 2007. 156

[42] Adrien Bartoli, Navneet Dalal, and Radu Horaud. Motion panoramas. In Journal of Computer Animation and Virtual Worlds, 15(5):501 517, nov 2004. 38

[43] Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. SURF Speeded Up Robust Features.

Computer Vision and Image Understanding (CVIU)

[44] Marco Bertini, Alberto Del Bimbo, and Walter NunziatiDipartimento di Sistemi e Informatica,“Player Identiﬁcation in Soccer Videos”University of Florence Via S. Marta, 3 - 50139 Florence, Italy.

[45] Ephrati & Navid Moghadam ,in the thesis of basketball player identification by jersey and number recognition Page (2,3).

[46] Carlo Tomasi, in the thesis of Image Correlation, Convolution and Filtering discussed about image correlation .page (2-6)

[47] Prathibha.M & Sheena Anees “ ocr based player jersey number identification for generating commentary from video” page(2,3).

[48] Marco Bertini, Alberto Del Bimbo, and Walter Nunziati Dipartimento di Sistema Informatics, University of Florence Via S. Marta, 3 50139 Florence, Italy ” Player Identification in SoccerVideos”.

[49] Roli Bansal, Priti Sehgal, and Punam Bedi “A Simplified Fuzzy Filter for Impulse Noise Removal using Thresholding”

[50] Vijaya Kumar Sagenela and C .Nagaraju Research Scholar, Department of Computer Science & Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India “Fuzzy Mean Filter for Immense Impulse Noise Removal”

Appendix

Here are the methods we used:

 RGB Color Thresholding

 Median filtering

 R-CNN

 HSV

 Map Detector

 Create templates

 Jersey recognition

 lines

 Number recognition

 OCR

 OCR_1

 OCR1

 Player recognition

 Non Linear Filtering

 Fuzzy Filtering

 First sub filtering

 Second sub filtering

 Read letter

 Read_letter1

 Remove Area

 Remove Box

Dalam dokumen This Report Presented in Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Computer Science and Engineering (Halaman 34-39)