An introduction to variational methods for graphical models
Learning in graphical models
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Framework for Modeling Appearance Change in Image Sequences
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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Color histograms are widely used for visual tracking due to their robustness against object deformations. However, traditional histogram representation often suffers from problems of partial occlusion, background cluttering and other appearance corruptions. In this paper, we propose a probabilistic index histogram to improve the discriminative power of the histogram representation. With this modeling, an input frame is translated into an index map whose entries indicate indexes to a separate bin. Based on the index map, we introduce spatial information and the bin-ratio dissimilarity in histogram comparison. The proposed probabilistic indexing technique, together with the two robust measurements, greatly increases the discriminative power of the histogram representation. Both qualitative and quantitative evaluations show the robustness of the proposed approach against partial occlusion, noisy and clutter background.