Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Information Retrieval
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Mouse Contours (without Too Many Samples)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A General Framework for Combining Visual Trackers --- The "Black Boxes" Approach
International Journal of Computer Vision
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Descriptor-Based Observation Model for Visual Tracking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
ACM Computing Surveys (CSUR)
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Fourier-based geometric shape prior for snakes
Pattern Recognition Letters
Tracking by Affine Kernel Transformations Using Color and Boundary Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Large Lump Detection Using a Particle Filter of Hybrid State Variable
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Optimum kernel function design from scale space features for object detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Solidity based local threshold for oil sand image segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contours for tracking distributions
IEEE Transactions on Image Processing
Proceedings of the 2012 ACM Research in Applied Computation Symposium
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This paper investigates kernel based tracking using shape information. A kernel based tracker typically models an object with a primitive geometric shape, and then estimates the object state by fitting the kernel such that the appearance model is optimized. Most of the appearance models in kernel based tracking utilize the textural information within the kernel, although a few of them also make use of the gradient information along the kernel boundary. Interestingly, shape information of a general form has never been fully exploited in kernel tracking, despite the fact that shape has been widely used in silhouette tracking at the cost of intensive computation. In this paper, we propose an original way to incorporate shape knowledge into the appearance model of kernel based trackers while preserving their computational advantage versus silhouette based trackers. Experimental results demonstrate that kernel tracking is strongly improved by exploiting the proposed shape cue through comparisons to both kernel and silhouette trackers.