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Paper Count: 30184
Tracking Objects in Color Image Sequences: Application to Football Images

Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

Abstract:

In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328784

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[1] J. Allen, R. Butterly, M. Welch and R. Wood, "The physical and physiological value of 5-a-side soccer training to 11-a-side match play". J. Hum. Move. Stud., vol. 34, pp.1-11 1998.
[2] T. Reilly, and V. Thomas, "A motion analysis of work-rate in different positional roles in professional football match play". J. Human Move. Stud., vol. 2, pp.87-97, 1976.
[3] R.T. Withers, Z. Maricic, S. Wasilewski and L. Kelly, "Match analyses of Australian professional soccer players". J. Human Move. Stud., Vol. 1, No. 2, pp. 205-222, 1982.
[4] S.R. Mayhew, and H.A. Wenger, "Time-motion analysis of professional soccer", J. Human Move. Stud., vol. 11, pp. 49-52, 1985.
[5] E. Hennig, and R. Briehle, "Game analysis by gps satellite tracking of soccer players". In Proc. XI Congress of Canadian Society of Biomechanics, Canada, July, 2000, pp.44.
[6] A. Elgammal, and L. Davis, "Probabilistic framework for segmenting people under occlusion", in Proceedings of the Eighth International Conference on Computer Vision IEEE, pp.9-12, 2001.
[7] C. Stauffer, "Estimating tracking sources and sinks", in: Proceedings of the IEEE Workshop on Event Mining, 2003.
[8] M. Xu, J. Orwell, L. Lowey and D. Thirde, "Architecture and algorithms for tracking football players with multiple cameras"', IEE Proceedings: Vision, Image and Signal Processing, Vol. 152, No. 2, pp. 232-241, 2005.
[9] I. Haritaoglu, D. Harwood, and L. Davis, "Who? when? where? what? a real time system for detecting and tracking people", in Proceedings of the International Conference on Automatic Face and Gesture Recognition, pp. 222-227, 1998.
[10] L. Bretzner, I. Laptev and T. Lindeberg, "Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering', in Proc. 5th IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 423-428, 2002.
[11] S. Park, and J. Aggarwal, "Segmentation and tracking of interacting human body parts under occlusion and shadowing", in: IEEE Workshop on Motion and Video Computing, pp. 105-111, 2002.
[12] P. Viola, and W.M. Wells, "Alignment by Maximization of Mutual Information", International Journal on Computer Vision, Vol. 24, No. 2, pp.137-154, 1997.
[13] J. Fisher, and J.C. Principe, "A Nonparametric Method for Information Theoretic Feature Extraction", Proc. Defense Advance Research Projects Agency (DARPA) Image Understanding Workshop, 1997.
[14] O. Javed, and M. Shah, "Tracking and object classification for automated surveillance", In Proceedings ECCV, 2002.
[15] Z. Kim, and J. Malik, "Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking", In Proceedings ICCV, 2003.
[16] C. Stauffer, and E. Grimson, "Learning patterns of activity using realtime tracking", IEEE Trans. PAMI, Vol. 22, No. 8, pp.747-757, 2000.
[17] A. Torralba, and P. Sinha, "Statistical context priming for object detection", In Proc. ICCV, pp. 763-770, 2001.
[18] S.J. McKenna, S. Jabri, Z. Duric, M, Rosenfeld, and A.H. Wechsler, "Tracking groups of people", Comput. Vision Image Understand, vol. 80, pp. 42-56, 2000.
[19] S. Lu, G. Tsechpenakis, and D. N. Metaxas, "Blob analysis of the head and hands: A method for deception detection", In 38th Hawaii International Conference on System Sciences, 2005.
[20] M. Sivabalakrishnan and D. Manjula. "Human Tracking and Segmentation using Color Space Conversion", International Journal of Computer Science Issues, Vol. 7, No. 5, pp. 284-288, 2010.
[21] K. Huang, J. Lin, J.A. Gajnak, and R.F. Murphy, "Image Content-based Retrieval and Automated Interpretation of Fluorescence Microscope Images via the Protein Subcellular Location Image Database", IEEE International Symposium on Biomedical Imaging, pp.325-328, 2002.
[22] A. Buzzanca, G. Castellano and A.M. Fanelli, "Feature-Driven Classification of Musical Styles". World Academy of Science, Engineering and Technology, vol. 57, pp. 471-475, 2009.
[23] W. S. Qureshi and N. Alvi. "Object Tracking using MACH filter and Optical Flow in Cluttered Scenes and Variable Lighting Conditions". World Academy of Science, Engineering and Technology, Vol. 60, pp. 709-712, 2009.
[24] G. Scott, and H. Longuet-Higgins, "An algorithm for associating the features of two images", in: Proceedings of the Royal Society of London B, Vol. 244, pp.21-26, 1991.
[25] I. Cox, "A review of statistical data association techniques for motion correspondence", Int. J. Comput. Vision, Vol. 10, No. 1, pp. 53-66, 1993.
[26] M. Pilu, "A direct method for stereo correspondence based on singular value decomposition", in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.261-266, 1997.
[27] G. Padmavathi, M. Muthukumar and S. K. Thakur. "Non linear Image segmentation using fuzzy c means clustering method with thresholding for underwater images", International Journal of Computer Science Issues, Vol. 7, No. 3, pp. 35-40, 2010.
[28] Colombari, A., Fusiello, A. and Murino V. "Segmentation and tracking of multiple video objects", Journal of pattern recognition, vol. 40, pp. 1307 - 1317, 2007.
[29] G.Padmavathi1, P. Subashini and A. Sumi. "Empirical Evaluation of Suitable Segmentation Algorithms for IR Images", International Journal of Computer Science Issues, Vol. 7, No. 4, pp. 22-29, 2010.
[30] H. H. Huynh, J. Meunier, J.Sequeira, and M. Daniel, "Real time detection, tracking and recognition of medication intake". World Academy of Science, Engineering and Technology, vol. 60, pp. 280-287, 2009.
[31] H. K. Kim, S. H. Park, D. H. Kim, and S. J. Ko, "Probabilistic Center Voting Method for Subsequent Object Tracking and Segmentation". World Academy of Science, Engineering and Technology, vol. 59, pp.450-454, 2009.
[32] M. Sujaritha and S. Annadurai, "Color Image Segmentation using Adaptive Spatial Gaussian Mixture Model". World Academy of Science, Engineering and Technology, vol. 61, pp. 744-748, 2010.