Abstract
Motion blurs are pervasive in real captured video data, especially for hand-held cameras and smartphone cameras because of their low frame rate and material quality. This paper presents a novel Kernel-based motion-Blurred target Tracking (KBT) approach to accurately locate objects in motion blurred video sequence, without explicitly performing deblurring. To model the underlying motion blurs, we first augment the target model by synthesizing a set of blurred templates from the target with different blur directions and strengths. These templates are then represented by color histograms regularized by an isotropic kernel. To locate the optimal position for each template, we choose to use the mean shift method for iterative optimization. Finally, the optimal region with maximum similarity to its corresponding template is considered as the target. To demonstrate the effectiveness and efficiency of our method, we collect several video sequences with severe motion blurs and compare KBT with other traditional trackers. Experimental results show that our KBT method can robustly and reliably track strong motion blurred targets.
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References
Badrinarayanan, V., Pérez, P., Clerc, F.L., Oisel, L.: Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues. In: IEEE International Conference on Computer Vision, ICCV (2007)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 25, 564–577 (2003)
Cai, J., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009)
Dai, S., Yang, M., Wu, Y., Katsaggelos, A.: Tracking Motion-Blurred Targets in Video. In: IEEE International Conference on Image Processing, ICIP (2006)
Fergus, R., Singh, B., Hertzmann, A., Roweis, S., Freeman, W.: Removing camera shake from a single photograph. ACM T. on Graphics, SIGGRAPH (2006)
Isard, M., Blake, A.: Condensation-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision (IJCV) 29, 5–28 (1998)
Jin, H., Favaro, P., Cipolla, R.: Visual Tracking in the Presence of Motion Blur. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2005)
Levin, A.: Blind motion deblurring using image statistics Advances. In: Advances in Neural Information Processing Systems, NIPS (2007)
Levin, A., Fergus, R., Durand, F., Freeman, W.: Image and depth from a conventional camera with a coded aperture. ACM T. on Graphics, SIGGRAPH (2007)
Lou, Y., Bertozzi, A., Soatto, S.: Direct Sparse Deblurring. Int’l. J. Math. Imaging and Vision (2010)
Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum Error Bounded Efficient â„“1 Tracker with Occlusion Detection. In: CVPR (2011)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Wu, Y., Wu, B., Liu, J., Lu, H.Q.: Probabilistic Tracking on Riemannian Manifolds. In: IEEE International Conference on Pattern Recognition, ICPR (2008)
Wu, Y., Wang, J.Q., Lu, H.Q.: Robust Bayesian tracking on Riemannian manifolds via fragments-based representation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (2009)
Wu, Y., Cheng, J., Wang, J.Q., Lu, H.Q.: Real-time visual tracking via incremental covariance tensor learning. In: IEEE International Conference on Computer Vision, ICCV (2009)
Richardson, W.: Bayesian-Based Iterative Method of Image Restoration. Journal of the Optical Society of America (JOSA) 62, 55–59 (1972)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4) (2006)
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Wu, Y., Hu, J., Li, F., Cheng, E., Yu, J., Ling, H. (2011). Kernel-Based Motion-Blurred Target Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_49
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DOI: https://doi.org/10.1007/978-3-642-24031-7_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24030-0
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