Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Description of interest regions with local binary patterns
Pattern Recognition
Incremental two-dimensional linear discriminant analysis with applications to face recognition
Journal of Network and Computer Applications
Finger vein recognition with manifold learning
Journal of Network and Computer Applications
Journal of Network and Computer Applications
Deform PF-MT: particle filter with mode tracker for tracking nonaffine contour deformations
IEEE Transactions on Image Processing
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Journal of Network and Computer Applications
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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Object tracking in the presence of appearance variation and occlusion is a hot topic in research, many algorithms were proposed in recent years. Early contour tracking algorithms used particle filter in a high dimensional space. In practice, contour points can move independently, hence contour deformation forms a high dimensional deformation space. As a result, the application of particle filter is calculation expensive. In this paper, we address the problem of tracking contour in complex environments by involving subspace and a contour template. Specifically, our algorithm tracks the global motion and the local contour deformation separately. We track the global motion by weighted distance to subspace, which is adaptive to the complex environment variation by incremental learning, and then use contour model to track local deformation and evolve the contour to the edge points. The experimental results show that our method can track object contour undergoing partially occlusion and shape deforming, which verify the effectiveness of the proposed algorithm.