Abstract
To build a robust visual tracking method it is important to consider issues such as low observation resolution and variation in the target object’s shape. When we capture an object moving fast in a video camera motion blur is observed. This paper introduces a visual trajectory estimation method using blur characteristics in the 3D space. We acquire a movement speed vector based on the shape of a motion blur region. This method can extract both the position and speed of the moving object from an image frame, and apply them to a visual tracking process using Kalman filter. We estimated the 3D position of the object based on the information obtained from two different viewpoints as shown in figure 1. We evaluated our proposed method by the trajectory estimation of a badminton shuttlecock from video sequences of a badminton game.
Chapter PDF
Similar content being viewed by others
References
Gandhi, H., Collins, M., Chuang, M., Narasimhan, P.: Real–Time Tracking of Game Assets in American Football for Automated Camera Selection and Motion Capture. Procedia Engineering 2(2), 2667–2673 (2010)
Lu, W.-L., Okuma, K., Little, J.J.: Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image and Vision Computing 27(1-2), 189–205 (2009)
Chen, H.-T., Tien, M.-C., Chen, Y.-W., Tsai, W.-J., Lee, S.-Y.: Physics-based ball tracking and 3D trajectory reconstruction with applications to shooting location estimation in basketball video. J. Vis. Commun. Image R 20(3), 204–216 (2009)
Chen, H.-T., Chen, H.-S., Hsiao, M.-H., Chen, Y.-W., Lee, S.-Y.: A Trajectory-Based Ball Tracking Framework with Enrichment for Broadcast Baseball Videos. In: International Computer Symposium (ICS 2006), Taiwan, vol. III, pp. 1145–1150 (December 2006)
Yan, F., Christmas, W., Kittler, J.: Layered Data Association Using Graph-Theoretic Formulation with Application to Tennis Ball Tracking in Monocular Sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1814–1830 (2008)
Ren, J., Orwell, J., Jones, G.A., Xu, M.: Tracking the soccer ball using multiple fixed cameras. Computer Vision and Image Understanding 113(5), 633–642 (2009)
Chen, H.-T., Tsai, W.-J., Lee, S.-Y.: Ball tracking and 3D trajectory approximation with applications to tactics analysis from single-camera volleyball sequences. Multimedia Tools and Applications 60(3), 641–667 (2012)
Alam, F., Chowdhury, H., Theppadungporn, C., Subic, A.: Measurements of Aerodynamic Properties of Badminton Shuttlecocks. Procedia Engineering 2(2), 2487–2492 (2010)
Jin, H., Favaro, P., Cipolla, R.: Visual Tracking in the Presence of Motion Blur. In: Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 18–25 (June 2005)
Park, Y., Lepetit, V., Woo, W.: Handling Motion-Blur in 3D Tracking and Rendering for Augmented Reality. IEEE Transactions on Visualization and Computer Graphics (TVCG) 18(9), 1449–1459 (2012)
Wu, Z., Hristov, N.I., Hedrick, T.L., Kunz, T.H., Betke, M.: Tracking a Large Number of Objects from Multiple Views. In: IEEE 12th International Conference on Computer Vision (ICCV 2009), pp. 1546–1553 (September-October 2009)
Karavasilis, V., Nikou, C., Likas, A.: Visual Tracking by Adaptive Kalman Filtering and Mean Shift. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS, vol. 6040, pp. 153–162. Springer, Heidelberg (2010)
Huang, C., Wu, B., Nevatia, R.: Robust Object Tracking by Hierarchical Association of Detection Responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)
Satoh, Y., Okatani, T., Deguchi, K.: A Color-based Tracking by Kalman Particle Filter. In: International Conference on Pattern Recognition (ICPR 2004), vol. 3, pp. 502–505 (August 2004)
Xu, X., Li, B.: Adaptive Rao-Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance. IEEE Transactions on Image Processing 16(3), 838–849 (2007)
Reilly, V., Idrees, H., Shah, M.: Detection and Tracking of Large Number of Targets in Wide Area Surveillance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 186–199. Springer, Heidelberg (2010)
Yang, C., Duraiswami, R., Davis, L.: Fast Multiple Object Tracking via a Hierarchical Particle Filter. In: IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, pp. 212–219 (October 2005)
Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1728–1740 (2008)
Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred Target Tracking by Blur-driven Tracker. In: IEEE International Conference on Computer Vision (ICCV), pp. 1100–1107 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shishido, H., Kitahara, I., Kameda, Y., Ohta, Y. (2014). A Trajectory Estimation Method for Badminton Shuttlecock Utilizing Motion Blur. In: Klette, R., Rivera, M., Satoh, S. (eds) Image and Video Technology. PSIVT 2013. Lecture Notes in Computer Science, vol 8333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53842-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-53842-1_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53841-4
Online ISBN: 978-3-642-53842-1
eBook Packages: Computer ScienceComputer Science (R0)