This article addresses the problem of multi-object tracking by using a non-deterministic model of target behaviors with hard constraints. To capture the evolution of target features as well as their locations, we permit objects to lie in a general topological target configuration space, rather than a Euclidean space. We obtain tracker performance bounds based on sample rates, and derive a flexible, agnostic tracking algorithm. We demonstrate our algorithm on two scenarios involving laboratory and field data.

翻译：本文通过使用带有硬约束的目标行为非确定性模型，解决了多目标跟踪问题。为捕捉目标特征及其位置的演化，我们允许对象位于一般的拓扑目标配置空间，而非欧几里得空间。基于采样率，我们获得了跟踪器的性能界限，并推导出一种灵活、与模型无关的跟踪算法。我们通过涉及实验室和现场数据的两种场景演示了该算法。