Dual-force metric learning for robust distracter-resistant tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Visual tracking via adaptive tracker selection with multiple features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Local appearance based robust tracking via sparse representation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Robust Visual Tracking via Structured Multi-Task Sparse Learning
International Journal of Computer Vision
Block covariance based l1 tracker with a subtle template dictionary
Pattern Recognition
Sparse coding based visual tracking: Review and experimental comparison
Pattern Recognition
An improved fisher discriminant dictionary learning for video object tracking
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Visual tracking in continuous appearance space via sparse coding
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
Hi-index | 0.00 |
Recently, sparse representation has been applied to visual tracking to find the target with the minimum reconstruction error from the target template subspace. Though effective, these L1 trackers require high computational costs due to numerous calculations for $/ell$_1 minimization. In addition, the inherent occlusion insensitivity of the $/ell$_1 minimization has not been fully utilized. In this paper, we propose an efficient L1 tracker with minimum error bound and occlusion detection which we call Bounded Particle Resampling (BPR)-L1 tracker. First, the minimum error bound is quickly calculated from a linear least squares equation, and serves as a guide for particle resampling in a particle filter framework. Without loss of precision during resampling, most insignificant samples are removed before solving the computationally expensive $/ell$_1 minimization function. The BPR technique enables us to speed up the L1 tracker without sacrificing accuracy. Second, we perform occlusion detection by investigating the trivial coefficients in the $/ell$_1 minimization. These coefficients, by design, contain rich information about image corruptions including occlusion. Detected occlusions enhance the template updates to effectively reduce the drifting problem. The proposed method shows good performance as compared with several state-of-the-art trackers on challenging benchmark sequences.