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Research Article | Open Access

Synthesis of a stroboscopic image from a hand-held camera sequence for a sports analysis

Keio University, Yokohama, 223-8522, Japan.
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Abstract

This paper presents a method for synthesizing a stroboscopic image of a moving sports player from a hand-held camera sequence. This method has three steps: synthesis of background image, synthesis of stroboscopic image, and removal of player’s shadow. In synthesis of background image step, all input frames masked a bounding box of the player are stitched together to generate a background image. The player is extracted by an HOG-based people detector. In synthesis of stroboscopic image step, the background image, the input frame, and a mask of the player synthesize a stroboscopic image. In removal of shadow step, we remove the player’s shadow which negatively affects an analysis by using mean-shift. In our previous work, synthesis of background image has been time-consuming. In this paper, by using the bounding box of the player detected by HOG and by subtracting the images for synthesizing a mask, computational speed and accuracy can be improved. These have contributed greatly to the improvement from the previous method. These are main improvements and novelty points from our previous method. In experiments, we confirmed the effectiveness of the proposed method, measured the player’s speed and stride length, and made a footprint image. The image sequence was captured under a simple condition that no other people were in the background and the person controlling the video camera was standing still, such like a motion parallax was not occurred. In addition, we applied the synthesis method to various scenes to confirm its versatility.

References

[1]
Strohrmann, C.; Harms, H.; Kappeler-Setz, C.; Troster, G. Monitoring kinematic changes with fatigue in running using body-worn sensors. IEEE Transactions on Information Technology in Biomedicine Vol. 16, No. 5, 983-990, 2012.
[2]
Ghasemzadeh, H.; Loseu, V.; Guenterberg, E.; Jafari, R. Sport training using body sensor networks: A statistical approach to measure wrist rotation for golf swing. In: Proceedings of the 4th International Conference on Body Area Networks, Article No. 2, 2009.
[3]
Oliveira, G.; Comba, J.; Torchelsen, R.; Padilha, M.; Silva, C. Visualizing running races through the multivariate time-series of multiple runners. In: Proceedings of XXVI Conference on Graphics, Patterns and Images, 99-106, 2013.
[4]
Eskofier, B. M.; Musho, E.; Schlarb, H. Pattern classification of foot strike type using body worn accelerometers. In: Proceedings of IEEE International Conference on Body Sensor Networks, 1-4, 2013.
[5]
Beetz, M.; Kirchlechner, B.; Lames, M. Computerized real-time analysis of football games. IEEE Pervasive Computing Vol. 4, No. 3, 33-39, 2005.
[6]
Hamid, R.; Kumar, R. K.; Grundmann, M.; Kim, K.; Essa, I.; Hodgins, J. Player localization using multiple static cameras for sports visualization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 731-738, 2010.
[7]
Lu, W.-L.; Ting, J.-A.; Little, J. J.; Murphy, K. P. Learning to track and identify players from broadcast sports video. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 35, No. 7, 1704-1716, 2013.
[8]
Perše, M.; Kristan, M.; Kovačič S.; Vučkovič, G.; Perš, J. A trajectory-based analysis of coordinated team activity in a basketball game. Computer Vision and Image Understanding Vol. 113, No. 5, 612-621, 2009.
[9]
Atmosukarto, I.; Ghanem, B.; Ahuja, S.; Muthuswamy, K.; Ahuja, N. Automatic recognition of offensive team formation in American football plays. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, 991-998, 2013.
[10]
Hasegawa, K.; Saito, H. Auto-generation of runner’s stroboscopic image and measuring landing points using a handheld camera. In: Proceedings of the 16th Irish Machine Vision and Image Processing, 169-174, 2014.
[11]
Ebdelli, M.; Meur, O. L.; Guillemot, C. Video inpainting with short-term windows: Application to object removal and error concealment. IEEE Transactions on Image Processing Vol. 24, No. 10, 3034-3047, 2015.
[12]
Farbman, Z.; Lischinski, D. Tonal stabilization of video. ACM Transactions on Graphics Vol. 30, No. 4, Article No. 89, 2011.
[13]
Lu, S.-P.; Ceulemans, B.; Munteanu, A.; Schelkens, P. Spatio-temporally consistent color and structure optimization for multiview video color correction. IEEE Transactions on Multimedia Vol. 17, No. 5, 577-590, 2015.
[14]
Brown, M.; Lowe, D. G. Automatic panoramic image stitching using invariant features. International Journal of Computer Vision Vol. 74, No. 1, 59-73, 2007.
[15]
Rother, C.; Kolmogorov, V.; Blake, A. “GrabCut”: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics Vol. 23, No. 3, 309-314, 2004.
[16]
Farbman, Z.; Hoffer, G.; Lipman, Y.; Cohen-Or, D.; Lischinsk, D. Coordinates for instant image cloning. ACM Transactions on Graphics Vol. 28, No. 3, Article No. 67, 2009.
[17]
Guo, R.; Dai, Q.; Hoiem, D. Single-image shadow detection and removal using paired regions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2033-2040, 2011.
[18]
Miyazaki, D.; Matsushita, Y.; Ikeuchi, K. Interactive shadow removal from a single image using hierarchical graph cut. In: Lecture Notes in Computer Science, Vol. 5994. Zha, H.; Taniguchi, R.; Maybank, S. Eds. Springer Berlin Heidelberg, 234-245, 2009.
[19]
Correa, C. D.; Ma, K.-L. Dynamic video narratives. ACM Transactions on Graphics Vol. 29, No. 4, Article No. 88, 2010.
[20]
Lu, S.-P.; Zhang, S.-H.; Wei, J.; Hu, S.-M.; Martin, R. R. Timeline editing of objects in video. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 7, 1218-1227, 2013.
[21]
Klose, F.; Wang, O.; Bazin, J.-C.; Magnor, M.; Sorkine-Hornung, A. Sampling based scene-space video processing. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 67, 2015.
[22]
Fukunaga, K.; Hostetler, L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory Vol. 21, No. 1, 32-40, 1975.
[23]
Comaniciu, D.; Meer, P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 5, 603-619, 2002.
[24]
Dalal, N.; Triggs, B. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 886-893, 2005.
[25]
Kalman, R. E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering Vol. 82, No. 1, 35-45, 1960.
[26]
Bay, H.; Ess, A.; Tuytelaars, T.; Gool, L. V. Speeded-up robust features (SURF). Computer Vision and Image Understanding Vol. 110, No. 3, 346-359, 2008.
[27]
Cho, S.-H.; Kang, H.-B. Panoramic background generation using mean-shift in moving camera environment. In: Proceedings of the International Conference on Image Processing, Computer Vision and Pattern Recognition, 829-835, 2011.
Computational Visual Media
Pages 277-289
Cite this article:
Hasegawa K, Saito H. Synthesis of a stroboscopic image from a hand-held camera sequence for a sports analysis. Computational Visual Media, 2016, 2(3): 277-289. https://doi.org/10.1007/s41095-016-0053-5

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Revised: 22 January 2016
Accepted: 10 April 2016
Published: 01 June 2016
© The Author(s) 2016

This article is published with open access at Springerlink.com

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