CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Robust and fast collaborative tracking with two stage sparse optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Real-time visual tracking using compressive sensing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Minimum error bounded efficient $/ell _1$ tracker with occlusion detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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Particle Filter is the most widely used framework for object tracking. Despite its advantages in handling complex cases, the discretization of the object appearance space makes it difficult to search the solution efficiently, and the number of particles is also greatly limited in consideration of computational cost, especially for some time-consuming object representations, e.g. sparse representation. In this paper, we propose a novel tracking method in which the appearance space is relaxed to be continuous, the solution then can be searched efficiently via sparse coding iteratively. As particle filter, our method can be combined with many generic tracking methods; typically, we adopt ℓ1 tracker, and demonstrate that with our method both its efficiency and accuracy can be improved in comparison to the version based on particle filter. Another advantage of our method is that it can handle dynamic change of object appearance by adaptively updating the object template model using the learned dictionary, and at the same time can avoid drifting by using representation error for supervision. Our method thus can perform more robust than previous methods in dynamic scenes of gradual changes. Both qualitative and quantitative evaluations demonstrate the efficiency and robustness of the proposed method.