Tracking and data association
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Tracking Non-Stationary Appearances and Dynamic Feature Selection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Comparison of target detection algorithms using adaptive background models
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Dual-force metric learning for robust distracter-resistant tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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
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We present a discriminative model that casts appearance modeling and visual matching into a single objective for visual tracking. Most previous discriminative models for visual tracking are formulated as supervised learning of binary classifiers. The continuous output of the classification function is then utilized as the cost function for visual tracking. This may be less desirable since the function is optimized for making binary decision. Such a learning objective may make it not to be able to well capture the manifold structure of the discriminative appearances. In contrast, our unified formulation is based on a principled metric learning framework, which seeks for a discriminative embedding for appearance modeling. In our formulation, both appearance modeling and visual matching are performed online by efficient gradient based optimization. Our formulation is also able to deal with multiple targets, where the exclusive principle is naturally reinforced to handle occlusions. Its efficacy is validated in a wide variety of challenging videos. It is shown that our algorithm achieves more persistent results, when compared with previous appearance model based tracking algorithms.