Order Matters: A Distributed Sampling Method for Multi-Object Tracking
Order Matters: A Distributed Sampling Method for Multi-Object Tracking
复制标题
顺序很重要：多目标跟踪的分布式采样方法
DOI：
10.5244/c.18.89
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
D. Gática
中科院分区：
文献类型：
--
作者：
Kevin Smith;D. Gática
多目标跟踪（MOT）是许多视觉应用中的一个重要问题。对于粒子滤波（PF）跟踪，随着跟踪对象数量的增加，随机抽样的搜索空间在维数上呈爆炸式增长。分区抽样（PS）通过对搜索空间进行分区，然后依次搜索每个分区来解决这个问题。但是，顺序加权重采样步骤会导致随着对象数量的增加而增加的贫困化效应。这种影响取决于探索分区的特定顺序，从而产生不稳定和不理想的性能。我们提出了一种搜索状态空间的方法，该方法通过定义一组混合组件，并使用一小组代表性对象排序中的一个在每个组件中执行PS，从而在对象之间公平分配这些贫困化效应。使用合成数据和真实数据，我们表明我们的方法保留了PS的整体性能并降低了计算成本，同时提高了贫化效应显著的场景的性能。
Multi-Object tracking (MOT) is an important problem in a number of vision applications. For particle filter (PF) tracking, as the number of objects tracked increases, the search space for random sampling explodes in dimension. Partitioned sampling (PS) solves this problem by partitioning the search space, then searching each partition sequentially. However, sequential weighted resampling steps cause an impoverishment effect that increases with the number of objects. This effect depends on the specific order in which the partitions are explored, creating an erratic and undesirable performance. We propose a method to search the state space that fairly distributes these impoverishment effects between the objects by defining a set of mixture components and performing PS in each of these components using one of a small set of representative object orderings. Using synthetic and real data, we show that our method retains the overall performance and reduced computational cost of PS, while improving performance in scenes where the impoverishment effect is significant.