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IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quality of Training Sample Estimates of the Bhattacharyya Coefficient
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
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CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
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Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
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Proceedings of the 24th DAGM Symposium on Pattern Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Multiple Object Tracking via a Hierarchical Particle Filter
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Covariance Tracking using Model Update Based on Lie Algebra
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Diffusion Distance for Histogram Comparison
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
EMD-L1: an efficient and robust algorithm for comparing histogram-based descriptors
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
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Adaptive Object Tracking Based on an Effective Appearance Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree-structured image difference for fast histogram and distance between histograms computation
Pattern Recognition Letters
Correlation-based incremental visual tracking
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A compact association of particle filtering and kernel based object tracking
Pattern Recognition
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The dissimilarity between a target descriptor and a particle descriptor is a crucial parameter in the particle filtering (PF), while the widely used Bhattacharyya dissimilarity (BD) is not discriminative enough. This paper presents an incremental Bhattacharyya dissimilarity (IBD) for measuring histogram based descriptors (HBDs) used for particle weight estimation. IBD is defined by incorporating an incremental similarity matrix (ISM) into the BD. Such an ISM imposes the incremental similarity beliefs on the matched bin patches of two input histograms and enables a cross-bin interaction during the comparison, which yields the enhanced capability of discriminating the particles located in the object from those positioned in the background. We propose a robust approach to compute the ISM by jointly utilizing the spatial and temporal attributes. Also, to handle target appearance changes and deformations, a classification-inspired target model update strategy is presented. These components lead to an effective and robust tracking algorithm. Experimental results demonstrate that IBD shows promising discriminative capability in comparison with other state of the art dissimilarity measures. Moreover, the IBD based PF-tracker also exhibits competitive tracking performance, especially under scenarios of partial occlusion and background clutter.