EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models Revisited
International Journal of Computer Vision
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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
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
Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compressive Structured Light for Recovering Inhomogeneous Participating Media
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Visual tracking based on Log-Euclidean Riemannian sparse representation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Robust visual tracking with structured sparse representation appearance model
Pattern Recognition
Dual-force metric learning for robust distracter-resistant tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Low-rank sparse learning for robust visual tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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
Robust visual tracking using dynamic classifier selection with sparse representation of label noise
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Visual tracking in continuous appearance space via sparse coding
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Efficient tracking using a robust motion estimation technique
Multimedia Tools and Applications
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The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods.