Tracking Objects in Color Image Sequences: Application to Football Images
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:49769", author = "Mourad Moussa and Ali Douik and Hassani Messaoud", title = "Tracking Objects in Color Image Sequences: Application to Football Images", 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.", volume = "5", number = "8", pages = "807-7", }
