An Optimized DBN-Based Mode-Focusing Particle Filter

机译：基于DBN的基于DBN的模型聚焦粒子滤波器

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We propose an original particle filtering-based approach combining optimization and decomposition techniques for sequential non-parametric density estimation defined in high-dimensional state spaces. Our method relies on Annealing to focus on the correct distributions and on probabilistic conditional independences defined by Dynamic Bayesian Networks to focus samples on their modes. After proving its theoretical correctness and showing its complexity, we highlight its ability to track single and multiple articulated objects both on synthetic and real video sequences. We show that our approach is particularly effective, both in terms of estimation errors and computation times.

机译：我们提出了一种基于粒子滤波的方法，组合优化和分解技术，用于在高维状态空间中定义的顺序非参数浓度估计。我们的方法依赖于退火，专注于正确的分布和动态贝叶斯网络定义的概率条件独立性，以将样本集中在其模式上。在证明其理论正确性并显示其复杂性后，我们突出了其在合成和真实视频序列上跟踪单个和多个铰接物体的能力。我们表明我们的方法在估计误差和计算时间方面都特别有效。

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《IEEE Conference on Computer Vision and Pattern Recognition》|2012年||共6页
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Severine Dubuisson; Christophe Gonzales;
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中图分类 TP391.41-53;
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4. An Optimized DBN-Based Mode-Focusing Particle Filter [C] . Severine Dubuisson, Christophe Gonzales IEEE Conference on Computer Vision and Pattern Recognition . 2012

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An Optimized DBN-Based Mode-Focusing Particle Filter

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