Multi-class Target Tracking Using the Semantic PHD Filter
Multi-class Target Tracking Using the Semantic PHD Filter
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DOI：
10.1007/978-3-030-95459-8_32
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发表时间：
2019
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影响因子：
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通讯作者：
Jun Chen-;P. Dames
中科院分区：
文献类型：
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作者：
Jun Chen-;P. Dames
为了使移动的机器人能够在复杂的动态环境中有效地操作，它必须能够理解它们周围的物体在哪里以及是什么。在本文中，我们引入了语义概率假设密度（SPHD）过滤器，它允许机器人同时跟踪多个类别的目标，尽管测量不确定性，包括假阳性检测、假阴性检测、测量噪声和目标错误分类。SPHD滤波器能够为每种类型的目标结合不同的运动模型，并且能够在目标数量未知且随时间变化的情况下发挥作用。我们证明了SPHD滤波器的有效性，通过模拟包含静态和动态目标的多种目标类型。我们表明，SPHD过滤器的性能优于并行运行的PHD过滤器的集合，每个目标类。
In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. We demonstrate the efficacy of the SPHD filter via simulations with multiple target types containing both static and dynamic targets. We show that the SPHD filter performs better than a collection of PHD filters running in parallel, one for each target class.