Kernel-based motion-blurred target tracking

Authors:
Yi Wu;Jing Hu;Feng Li;Erkang Cheng;Jingyi Yu;Haibin Ling
Affiliations:
Jiangsu Engineering Center of Network Monitoring, Nanjing Univ. of Information Science & Technology, Nanjing and School of Computer & Software and Center for Inf. Science and Technology, C ...;Network Center, Nanjing University of Information Science & Technology, Nanjing;Department of Computer and Information Sciences, University of Delaware, Newark, DE;Center for Information Science and Technology, Computer and Information Science Department, Temple University, Philadelphia, PA;Department of Computer and Information Sciences, University of Delaware, Newark, DE;Center for Information Science and Technology, Computer and Information Science Department, Temple University, Philadelphia, PA
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Year:
2011

Citing 9
Cited 0

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Color-Based Probabilistic Tracking

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Tracking in the Presence of Motion Blur

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Removing camera shake from a single photograph

ACM SIGGRAPH 2006 Papers
Object tracking: A survey

ACM Computing Surveys (CSUR)
Image and depth from a conventional camera with a coded aperture

ACM SIGGRAPH 2007 papers
Robust Bayesian tracking on Riemannian manifolds via fragments-based representation

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Direct Sparse Deblurring

Journal of Mathematical Imaging and Vision

Quantified Score

Hi-index	0.00

Visualization

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.