论文标题
贝叶斯非参数建模，用于预测多个对象跟踪中的动态依赖性
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking
论文作者
论文摘要
跟踪多个对象的一些具有挑战性的问题包括时间相关的基数，无序测量和对象参数标记。在本文中，我们采用贝叶斯贝叶斯非参数方法来应对这些挑战。特别是，我们建议使用依赖的dirichlet和pitman-yor过程对多对象参数状态进行建模。与现有方法相比，这些非参数模型已被证明更加灵活，更健壮，用于估计对象变化数量的对象数量，从而为对象关联提供对象标记和识别测量值。然后提出蒙特卡洛抽样方法，以有效地从嘈杂的测量中学习对象的轨迹。使用模拟，与现有算法（如广义标记的多重bernoulli滤镜）相比，我们证明了新方法的估计性能优势。
Some challenging problems in tracking multiple objects include the time-dependent cardinality, unordered measurements and object parameter labeling. In this paper, we employ Bayesian Bayesian nonparametric methods to address these challenges. In particular, we propose modeling the multiple object parameter state prior using the dependent Dirichlet and Pitman-Yor processes. These nonparametric models have been shown to be more flexible and robust, when compared to existing methods, for estimating the time-varying number of objects, providing object labeling and identifying measurement to object associations. Monte Carlo sampling methods are then proposed to efficiently learn the trajectory of objects from noisy measurements. Using simulations, we demonstrate the estimation performance advantage of the new methods when compared to existing algorithms such as the generalized labeled multi-Bernoulli filter.
