A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
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
Distinctive Image Features from Scale-Invariant Keypoints
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
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Efficient Optimal Kernel Placement for Reliable Visual Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient Maximally Stable Extremal Region (MSER) Tracking
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 2
Recent advances and trends in visual tracking: A review
Neurocomputing
Selective spatio-temporal interest points
Computer Vision and Image Understanding
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 by proto-objects
Pattern Recognition
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
A novel particle filter with implicit dynamic model for irregular motion tracking
Machine Vision and Applications
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
Computer Vision and Image Understanding
Weighted attentional blocks for probabilistic object tracking
The Visual Computer: International Journal of Computer Graphics
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A major reason leading to tracking failure is the spatial distractions that exhibit similar visual appearances as the target, because they also generate good matches to the target and thus distract the tracker. It is in general very difficult to handle this situation. In a selective attention tracking paradigm, this paper advocates a new approach of discriminative spatial attention that identifies some special regions on the target, called attentional regions (ARs). The ARs show strong discriminative power in their discriminative domains where they do not observe similar things. This paper presents an efficient two-stage method that divides the discriminative domain into a local and a semi-local one. In the local domain, the visual appearance of an attentional region is locally linearized and its discriminative power is closely related to the property of the associated linear manifold, so that a gradient-based search is designed to locate the set of local ARs. Based on that, the set of semi-local ARs are identified through an efficient branch-and-bound procedure that guarantees the optimality. Extensive experiments show that such discriminative spatial attention leads to superior performances in many challenging target tracking tasks.