A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
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
Efficient visual object tracking with online nearest neighbor classifier
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
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The paper proposes a parametric approach for color based tracking. The method fragments a multimodal color object into multiple homogeneous, unimodal, fragments. The fragmentation process consists of multi level thresholding of the object color space followed by an assembling. Each homogeneous region is then modelled using a single parametric distribution and the tracking is achieved by fusing the results of the multiple parametric distributions. The advantage of the method lies in tracking complex objects with partial occlusions and various deformations like non-rigid, orientation and scale changes. We evaluate the performance of the proposed approach on standard and challenging real world datasets.