Widynski Nicolas, Dubuisson Séverine, Bloch Isabelle
Laboratory of Computer Sciences (UPMC-LIP6), University Pierre and Marie Curie (Paris 6), Paris, France.
IEEE Trans Syst Man Cybern B Cybern. 2011 Jun;41(3):635-49. doi: 10.1109/TSMCB.2010.2064767.
In this paper, we propose a novel method to introduce spatial information in particle filters. This information may be expressed as spatial relations (orientation, distance, etc.), velocity, scaling, or shape information. Spatial information is modeled in a generic fuzzy-set framework. The fuzzy models are then introduced in the particle filter and automatically define transition and prior spatial distributions. We also propose an efficient importance distribution to produce relevant particles, which is dedicated to the proposed fuzzy framework. The fuzzy modeling provides flexibility both in the semantics of information and in the transitions from one instant to another one. This allows one to take into account situations where a tracked object changes its direction in a quite abrupt way and where poor prior information on dynamics is available, as demonstrated on synthetic data. As an illustration, two tests on real video sequences are performed in this paper. The first one concerns a classical tracking problem and shows that our approach efficiently tracks objects with complex and unknown dynamics, outperforming classical filtering techniques while using only a small number of particles. In the second experiment, we show the flexibility of our approach for modeling: Fuzzy shapes are modeled in a generic way and allow the tracking of objects with changing shape.
在本文中，我们提出了一种在粒子滤波器中引入空间信息的新方法。该信息可以表示为空间关系（方向、距离等）、速度、缩放比例或形状信息。空间信息在通用模糊集框架中建模。然后将模糊模型引入粒子滤波器，并自动定义转移和先验空间分布。我们还提出了一种有效的重要性分布来生成相关粒子，该分布专门用于所提出的模糊框架。模糊建模在信息语义和从一个时刻到另一个时刻的转移方面都提供了灵活性。这使得人们能够考虑到被跟踪物体以相当突然的方式改变方向以及动力学先验信息不足的情况，如在合成数据上所展示的那样。作为示例，本文对真实视频序列进行了两项测试。第一项测试涉及一个经典的跟踪问题，结果表明我们的方法能够有效地跟踪具有复杂且未知动力学的物体，在仅使用少量粒子的情况下优于传统滤波技术。在第二项实验中，我们展示了我们的方法在建模方面的灵活性：模糊形状以通用方式建模，并允许对形状变化的物体进行跟踪。
