Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Tracking and Vehicle Classification via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context tracker: Exploring supporters and distracters in unconstrained environments
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-time visual tracking using compressive sensing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Decoding by linear programming
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Robust tracking with weighted online structured learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Real-Time Compressive Tracking utilizes a very spare measurement matrix to extract the features for the appearance model. Such model performs well when the tracked objects are well defined. However, when the objects are low-grain, low-resolution, or small, a fixed size sparse measurement matrix is not sufficient enough to preserve the image structure of the object. In this work, we propose a Dynamic Compressive Tracking algorithm that employs adaptive random projections that preserve the image structure of the objects during tracking. The proposed tracker uses a dynamic importance ranking weight to evaluate the classification results obtained by each of the sparse measurement matrices and complete the tracking with the optimal sparse matrix. Extensive experimental results, on challenging publicly available data sets, shows that the proposed dynamic compressible tracking algorithm outperforms conventional compressive tracker.