一种有效的标记/非标记随机有限集多目标跟踪算法
An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking
摘要：
我们提出了一种有效的随机有限集（RFS）为基础的多目标跟踪算法，其中对象的状态是由一个标记的多伯努利（LMB）RFS和泊松RFS的组合建模。该算法的计算要求较低的泊松部分用于跟踪不太可能存在的潜在对象。只有当表征对象存在的可扩展性的量高于阈值时，才创建新的标记的伯努利分量，并且通过算法的更准确但计算要求更高的LMB部分来跟踪对象。相反，如果相应的存在概率低于另一个阈值，则标记的伯努利分量被转移回泊松RFS。与现有的基于多Bernoulli和Poisson RFS的混合算法相反，该方法有利于跟踪连续性并实现复杂度降低功能。仿真结果表明，一个大的复杂度降低相对于其他基于RFS的算法具有可比的性能。
英文摘要：
We propose an efficient random finite set (RFS) based algorithm for multiobject tracking in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.
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