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Study of multi-targets tracking algorithm based on proposed particle filtering

机译:基于提出的粒子滤波的多目标跟踪算法研究

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Aiming at data correlation and estimation problems in particle filtering multi-targets tracking, classical particle filtering is extended into multi-targets state estimation in the given several observation process. Gibbs sampling is regarded as the methods of estimation and allocation correlation vector. Target state vector and association probability was jointly estimated without list, trim, threshold and other algorithms. This avoids merger drawbacks. Test is running in real video sequence. Stable tracking is realized under the complex tracking conditions. Experiments show that algorithms have strong the ability of solving data association problems.
机译:针对粒子滤波多目标跟踪中的数据相关性和估计问题,在给定的几个观测过程中,经典粒子滤波扩展为多目标状态估计。 Gibbs采样被视为估计和分配相关向量的方法。目标状态向量和关联概率是联合估计的,无需列表,修整,阈值和其他算法。这避免了合并的弊端。测试以真实视频序列运行。在复杂的跟踪条件下可以实现稳定的跟踪。实验表明,该算法具有很强的解决数据关联问题的能力。

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