AUTHORS: Vladimir Pleština, Vladan Papić

Download as PDF

ABSTRACT: This paper presents a new approach for tracking template-based objects based on spiral particle distribution algorithm. Proposed algorithm uses points on Archimedean spiral as possible location of object in next frame. Before applying algorithm, the system is provided with off-line learning of training data. After that, start point is initialized and algorithm for tracking is applied. Tracking starts from initialization location and searches for the best matching point on spiral as a new starting point in the next frame. In this work our algorithm is explained and compared with basic particle filter tracking algorithm. Experiment is demonstrated with real data on Croatian popular amateur game.

KEYWORDS: Particle filter, template-based tracking, spiral particle distribution

REFERENCES:

[ 1] A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” Acm Computing Surveys (CSUR), vol. 38, no. 4, 2006.

[2] J. Pers and S. Kovacic, “Computer vision system for tracking players in sports games,” IWISPA 2000. Proceedings of the First International Workshop on Image and Signal Processing and Analysis. in conjunction with 22nd International Conference on Information Technology Interfaces. (IEEE Cat. No.00EX437), vol. 1, pp. 177–182, 2000.

[3] J Liu, X Tong, W Li, T Wang, Y Zhang, and H Wang, “Automatic player detection, labeling and tracking in broadcast soccer video,” Pattern Recognition Letters, vol. 30, no. 2, pp. 103–113, Jan. 2009.

[4] Junqiu Wang and Yasushi Yagi, “Integrating color and shape-texture features for adaptive real-time object tracking.,” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 17, no. 2, pp. 235–40, Feb. 2008.

[5] C.J. Needham and R.D. Boyle, “Tracking multiple sports players through occlusion, congestion and scale,” British Machine Vision Conference, 2001.

[6] Jacky C. Yuk, Kwan-yee Wong, Ronald Y. Chung, F. L. Chin, and K. Chow, “Real-time Multiple Head Shape Detection and Tracking System with Decentralized Trackers,” Sixth International Conference on Intelligent Systems Design and Applications, pp. 384–389, Oct. 2006.

[7] K. Esther, E. Koller-meier, and L. V. Gool, “A Color-based Particle Filter,” Technology, pp. 53–60, 2002.

[8] W. Lu, K. Okuma, and J. Little, “Tracking and recognizing actions of multiple hockey players using the boosted particle filter,” Image and Vision Computing, vol. 27, no. 1-2, pp. 189–205, Jan. 2009.

[9] S. Saha, N. Bambha, and S. Bhattacharyya, “Design and implementation of embedded computer vision systems based on particle filters,” Computer Vision and Image Understanding, vol. 114, no. 11, pp. 1203–1214, Nov. 2010.

[10] C. Orrite-Urunuela and JE. Herrero, “An efficient particle filter for color-based tracking in complex scenes,” Advanced Video and, pp. 1–6, 2008.

[11] Chong Chen and Dan Schonfeld, “A particle filtering framework for joint video tracking and pose estimation.,” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 19, no. 6, pp. 1625–34, June 2010.

[12] E. Monier, P. Wilhelm, and U. Ruckert, “Template matching based tracking of players in indoor team sports,” 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6, Aug. 2009.

[13] V. Pleština, H. Dujmic, and V. Papic, “Proposal of a modular system for tracking indoor and outdoor sports,” Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization, pp. 132–137, 2009.

[14] K. Smith, D. Gatica-Perez, and J. Odobez, “Using Particles to Track Varying Numbers of Interacting People,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 00, no. c, pp. 962–969, 2005.

[15] A. Dearden, Y. Demiris, and O. Grau, “Tracking football player movement from a single moving camera using particle filters,” 3rd European Conference on Visual Media Production (CVMP 2006). Part of the 2nd Multimedia Conference 2006, pp. 29–37, 2006.

[16] M. Perˇse, M. Kristan, J. Perš, and S. Kovačič, “A template-based multi-player action recognition of the basketball game,” Proceedings of the ECCV Workshop on Computer Vision Based Analysis in Sport Environments, pp. 71–82, 2006.

[17] J.P. Lewis, “Fast normalized cross-correlation,” Vision Interface, vol. 10, no. 1, pp. 120–123, 1995.

[18] K. Briechle and U.D. Hanebeck, “Template matching using fast normalized cross correlation,” Proceedings of SPIE, vol. 4387, pp. 95– 102, 2001.

[19] D. Tsai, “Fast normalized cross correlation for defect detection,” Pattern Recognition Letters, vol. 24, no. 15, pp. 2625–2631, Nov. 2003.

[20] Calin Rotaru, Thorsten Graf, and Jianwei Zhang, “Color image segmentation in HSI space for automotive applications,” Journal of Real-Time Image Processing, vol. 3, no. 4, pp. 311–322, Mar. 2008.

[21] A. Sankaranarayanan, A. Srivastava, and R. Chellappa, “Algorithmic and architectural optimizations for computationally efficient particle filtering.,” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 17, no. 5, pp. 737–48, May 2008.

[22] S. Wei and S. Lai, “Fast template matching based on normalized cross correlation with adaptive multilevel winner update.,” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol. 17, no. 11, pp. 2227–35, Nov. 2008.

[23] W. Askar, O. Elmowafy, A. Youssif, G. Elnashar, 'Real-time UAV Target Tracking System Based on Optical Flow and Particle Filter Integration', WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 13, Art. #19, pp. 172-181, 2017.

[24] Z. Chen, Y. Bo, Y. Qu, X. Ling, X. Tao, Y. Liu 'Dynamic Population Adaptive Particle Swarm Optimized Particle Filter for Integrated Navigation', WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 11, Art. #28, pp. 235-244, 2015