A methodology for detection and estimation in the analysis of golf putting

Pattern Analysis and Applications - Tập 16 - Trang 459-474 - 2012
Micael S. Couceiro1,2, David Portugal1, Nuno Gonçalves1, Rui Rocha1, J. Miguel A. Luz2, Carlos M. Figueiredo2, Gonçalo Dias3
1Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
2RoboCorp, Electrotechnics Engineering Department, Engineering Institute of Coimbra, Coimbra, Portugal
3RoboCorp, Faculty of Sport Sciences and Physical Education, University of Coimbra, University Stadium of Coimbra, Coimbra, Portugal

Tóm tắt

This paper presents a methodology for visual detection and parameter estimation to analyze the effects of the variability in the performance of golf putting. A digital camera was used in each trial to track the putt gesture. The detection of the horizontal position of the golf club was performed using a computer vision technique, followed by an estimation algorithm divided in two different stages. On a first stage, diverse nonlinear estimation techniques were used and evaluated to extract a sinusoidal model of each trial. Secondly, several expert golf player trials were analyzed and, based on the results of the first stage, the Darwinian particle swarm optimization (DPSO) technique was employed to obtain a complete kinematical analysis and a characterization of each player’s putting technique. In this work, it is intended not only to test the performance of the DPSO method, but also to present a novel study in this field by identifying a putting “signature” of each player.

Tài liệu tham khảo

“Merriam-Webster Online”. http://www.merriam-webster.com/ (last visited in August 2010) Chiviacowsky S, Pinho TS, Alves D, Schild JFS (2008) Feedback autocontrolado: efeitos na aprendizagem de uma habilidade motora específica do golfe. Revista Brasileira de Educação Física e Esporte 22(4):265–271 Guadagnoli MA, Holcomb WR, Weber TJ (1999) The relationship between contextual interference effects and performer expertise on the learning of putting task. J Hum Mov Stud 37:19–36 Horner K, Fitzpatrick K, Smyth P (2008) The effect of increasing contextual interference on the practising of a motor skill”. In: Cabri J, Alves F, Araújo D, Barreiros J, Diniz J, Veloso A (eds) Book of abstracts. 13th annual congress of the ECSS. Sport Science by the Sea, Estoril, p A-71 Maxwell JP, Masters RSW, Eves FF (2000) From novice to no know-how: a longitudinal study of implicit motor learning. J Sports Sci 18:111–120 Porter JM, Magill RA (2005) Practicing along the contextual interference continuum increases performance of a golf putting task. J Exerc Psychol 27:S-124 Hume PA, Keogh J, Reid D (2005) The role of biomechanics in maximising distance and accuracy of golf shots. Sports Med 35(5):429–449 Nesbit SM, Hartzell TA, Nalevanko JC, Starr RM (1996) A discussion of iron golf club head inertia tensors and their effects on the golfer. J Appl Biomech 12(4):449–469 Pelz D (1989) Putt like the pros. Harper Collins, New York Pelz D (2000) Putting Bible: the complete guide to mastering the green. Publication Doubleday, New York Porter JM (2008) Systematically increasing contextual interference is beneficial for learning novel motor skills. A dissertation submitted to the graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfilment of the requirements for the degree of doctor of philosophy. The Department of Kinesiology, pp 1–283 McCarty JD (2002) A descriptive analysis of golf putting: what variables affect accuracy? Master of Science thesis. Purdue University, USA Mendes R, Martins R, Dias G (2008) Effects of a contextual interference continuum on golf putting task. Cabri J, Alves F, Araújo D, Barreiros J, Diniz J, Veloso A (eds) Book of abstracts. In: 13th annual congress of the ECSS. Sport Science by the Sea, Estoril, p. A-490 Perner P (2001) Motion tracking of animals for behavior analysis. In: Proceedings of the 4th international workshop on visual form. Lecture Notes in Computer Science, pp 779–786 Chen D, Yang J (2007) Robust object tracking via online dynamic spatial bias appearance models. IEEE Trans Pattern Anal Mach Intell 29:2157–2169 Batista J, Peixoto P, Fernandes C, Ribeiro M (2006) A dual-stage robust vehicle detection and tracking for real-time traffic monitoring. In: 9th international IEEE conference on intelligent transportation systems (ITSC), Toronto, pp 17–20 Javed O, Shah M (2003) KNIGHT: a multi-camera surveillance system. In: IEEE international conference on multimedia and expo 2003, Baltimore, pp 649–652 Gandhi T, Trivedi M (2007) Pedestrian protection systems: issues, survey and challenges. IEEE Trans Intell Transp Syst 8(3):413–430 Gerónimo D, López AM, Sappa AD, Graf T (2010) Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans Pattern Anal Mach Intell 32:1239–1258 Abdelkader M, Chellappa R, Zheng Q (2006) Integrated motion detection and tracking for visual surveillance. In: Proceedings of the 4th IEEE international conference on computer vision systems (ICVS 2006), pp 28–34 Cheng Y (1995) Mean shift, mode seeking and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799 Bradski G (1998) Computer vision face tracking as a component of a perceptual user interface. Workshop on Applications of Computer Vision, Princeton, pp 214–219 Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 22(5):603–619 Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575 Pérez P, Vermaak J, Blake A (2004) Data fusion for tracking with particles. Proc IEEE 92(3):495–513 Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32–40 Chang C, Ansari R (2005) Kernel particle filter for visual tracking. IEEE Signal Process Lett 12(3):242–245 Maggio E, Cavallaro A (2005) Hybrid particle filter and mean shift tracker with adaptive transition model. In: Proceedings of ICASSP Han B, Comaniciu D, Zhu Y, Davis LS (2004) Incremental density approximation and kernel-based bayesian filtering for object tracking. CVPR 1:638–644 Shan C, Tan T, Wei Y (2007) Real-time hand tracking using a mean shift embedded particle filter. J Pattern Recognit 40(7):1958–1970 Cai Y, de Freitas N, Little JJ (2006) Robust visual tracking for multiple targets. In: Proceedings of European conference on computer vision, pp 107–118 Bai K, Liu W (2007) Improved object tracking with particle filter and mean shift. In: Proceedings of the IEEE international conference on automation and logistics, Jinan, vol 2, pp 221–224 Brasnett P, Mihaylova L, Canagarajah N, Bull D (2005) Particle filtering with multiple cues for object tracking in video sequences. In: Proceedings of SPIE’s 17th annual symposium on electronic imaging, science and technology, V. 5685, pp 430–441 Brasnett P, Mihaylova L, Canagarajah N, Bull D (2007) Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image Vis Comput 25(8):1217–1227 Simon M, Behnke S, Rojas R (2000) Robust real time color tracking. In: 4th international workshop on RoboCup (robot world cup soccer games and conferences). Lecture Notes in Computer Science, pp 239–248 Pérez A, López F, Benlloch J, Christensen S (2009) Colour and shape analysis techniques for weed detection in cereal fields. Comput Electron Agric 69(1):73–79 Vezhnevets V, Sazonov V, Andreeva A (2003) A survey on pixel-based skin color detection techniques. Proc Graph 2003:85–92 Darrell T, Gordon G, Harville M, Woodfill J (2000) Integrated person tracking using stereo, color, and pattern detection. Int J Comput Vis 37(2):175–185 Yan F, Christmas W, Kittler J (2008) Layered data association using graph-theoretic formulation with application to tennis ball tracking in monocular sequences. IEEE Trans Pattern Anal Mach Intell 30(10) Wolf JK, Viterbi AM, Dixon SG (1989) Finding the best set of k paths through a trellis with application to multitarget tracking. IEEE Trans Aerosp Electron Syst 25:287–296 Quach T, Farooq M (1994) Maximum likelihood track formation with the Viterbi algorithm. In: IEEE conference on decision and control, pp 271–276 Luber M, Arras KO, Plagemann C, Burgard W (2009) Classifying dynamic objects: an unsupervised learning approach. Auton Robots Lucey S, Matthews I (2006) Face refinement through a gradient descent alignment approach. In: Proceedings of the HCSNet workshop on use of vision in human–computer interaction, Canberra, vol 56, pp 43–49 Momma M, Bennet K (2002) Pattern search methodology for support vector machines model selection. In: Proceedings of the SIAM international conference on data mining, Arlington Zhou H, Seyfarth B (2005) A pattern search method for image registration. Lecture Notes in Computer Science, SpringerLink, vol 3514, pp 664–670 Emery L, Borland M, Shang H (2003) Use of a general-purpose optimization module in accelerator control. In: Proceedings of the 20th particle accelerator conference, Portland, p 2330 Miura K, Hashimoto K, Inooka H, Gangloff J, Matheli M (2006) Model-less visual servoing using modified simplex optimization. Artif Life Robot 10(2):131–135 Tang J, Zhu J, Sun Z (2005) A novel path panning approach based on AppART and particle swarm optimization. In: Proceedings of the 2nd international symposium on neural networks, LNCS, vol 3498, pp 253–258 Solteiro Pires EJ, de Moura Oliveira PB, Tenreiro Machado JA, Boaventura Cunha J (2006) Particle swarm optimization versus genetic algorithm in manipulator trajectory planning. In: 7th Portuguese conference on automatic control Couceiro MS, Mendes R, Fonseca Ferreira NM, Tenreiro Machado JA (2009) Control optimization of a robotic bird. EWOMS’09, Lisbon Alrashidi MR, El-Hawary ME (2009) A survey of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13(4):913–918 Couceiro MS, Luz JMA, Figueiredo CM, Ferreira NMF, Dias G (2010) Parameter estimation for a mathematical model of the golf putting. In: Proceedings of WACI’10. Workshop applications of computational intelligence 2010. ISEC.IPC, Coimbra, pp 1–8. ISSN 978-989-8331-10-6 Shi Y, Eberhart R (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, vol 1, pp 101–106 Pires EJS, Machado JAT, Oliveira PBM, Cunha JB, Mendes L (2010) Particle swarm optimization with fractional-order velocity. J Nonlinear Dyn 61(295–301):2010 Blackwell T, Bentley P (2002) Don’t push me! Collision-avoiding swarms. In: Proceedings of IEEE congress on evolutionary computation, vol 2, pp 1691–1696 Krink T, Vesterstrom J, Riget J (2002) Particle swarm optimization with spatial particle extension. In: Proceedings of IEEE congress on evolutionary computation, vol 2, pp 1474–1479 Miranda V, Fonseca N (2002) New evolutionary particle swarm algorithm (EPSO) applied to voltage/VAR control. In: Proceedings of the 14th power systems computational conference Lovbjerg M, Krink T (2002) Extending particle swarms with self-organized criticality. In: Proceedings of IEEE congress on evolutionary computation, vol 2, pp 1588–1593 Chia-Feng J (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B Cybern 34(2):997–1006 Angeline P (1998) Using selection to improve particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 84–89 Zhang W, Xie X (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: Proceedings of IEEE international conference on systems, man, and cybernetics, vol 4, pp 3816–3821 Kannan S, Slochanal S, Padhy N (2004) Application of particle swarm optimization technique and its variants to generation expansion problem. Electr Power Syst Res 70(3):203–210 Tillett T, Rao TM, Sahin F, Rao R (2005) Darwinian particle swarm optimization. In: Proceedings of the 2nd Indian international conference on artificial intelligence, Pune, pp 1474–1487 Couceiro MS, Figueiredo CM, Ferreira NMF, Machado JAT (2008) Simulation of a robotic bird. Fractional differentiation and its applications, Ankara Knudson DV, Morrison CS (2002) Qualitative analysis of human movement. Human Kinetics Publishers Paradisis G, Rees J (2000) Kinematic analysis of golf putting for expert and novice golfers. In: Proceedings of the 18th international symposium on biomechanics in sports, Hong Kong Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, Nagoya Yang X-S (2010) Test problems in optimization. In: Yang X-S (ed) Engineering optimization: an introduction with metaheuristic applications. Wiley, Hoboken