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