Combined Data Association and Evolving Particle Filter for Tracking of Multiple Articulated Objects
Combined Data Association and Evolving Particle Filter for Tracking of Multiple Articulated Objects
复制标题
DOI：
10.1155/2011/642532
复制
发表时间：
2011-02
影响因子：
2.4
通讯作者：
H. Bhaskar;L. Mihaylova
中科院分区：
文献类型：
--
作者：
H. Bhaskar;L. Mihaylova
提出了一种结合数据关联和进化种群粒子滤波的多关节目标跟踪方法。视觉目标被表示为使用部件集合及其几何模型的图形结构。跟踪视频中的多个目标涉及从杂波中选择属于目标的有效测量或所有落在验证门内的其他测量的迭代交替方案。设计了一种扩展似然概率数据关联和演化粒子群组表示多部分分布的算法。在采样和重采样步骤中，使用受约束的遗传算子来引入粒子的多样性。我们探讨了各种模型参数对系统性能的影响，结果表明，该模型在标准数据集上取得了比其他广泛使用的方法更好的精度。
This paper proposes an approach for tracking multiple articulated targets using a combined data association and evolving population particle filter. A visual target is represented as a pictorial structure using a collection of parts together with a model of their geometry. Tracking multiple targets in video involves an iterative alternating scheme of selecting valid measurements belonging to a target from a clutter or other measurements that all fall within a validation gate. An algorithm with extended likelihood probabilistic data association and evolving groups of populations of particles representing a multiple-part distribution is designed. Variety in the particles is introduced using constrained genetic operators both in the sampling and resampling steps. We explore the effect of various model parameters on system performance and show that the proposed model achieves better accuracy than other widely used methods on standard datasets.