动态群体建模
Modeling Dynamic Swarms
摘要：
提出了动态群体视频序列的建模问题。我们将DS定义为动态对象（群元素）的随机重复空间配置的大布局，其运动表现出局部时空相互依赖性和平稳性，即，运动在任何小的时空邻域中都是相似的。DS的实例在自然界中大量存在，例如，成群的动物和成群的鸟。为了捕获DS的局部时空特性，我们提出了一个概率模型，该模型可以学习群元素的空间布局及其被建模为线性变换的联合动态。为此，时空邻域与每个群元素相关联，其中局部平稳性在空间和时间上都被强制执行。我们假设群动力学的先验分布根据MRF在空间和时间。将该模型嵌入MAP框架中，我们在学习群体的空间布局及其动态之间进行了权衡。我们使用ICM学习群变换，ICM在估计这些变换和更新它们在时空邻域中的分布之间迭代。我们证明了我们的方法的有效性进行实验，对真实的视频序列。鸟类，鹅，机器人群和行人的真实序列评估我们的模型对现实世界数据的适用性。
英文摘要：
This paper proposes the problem of modeling video sequences of dynamic swarms (DS). We define DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data.
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