Hierarchy and leadership interactions commonly occur in animal groups, crowds of people, and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g., final destination, environmental constraints, and best routes) or pertinent traits (e.g., better navigation, sensing, and decision making capabilities) compared with the rest of the group. This article presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader–follower model is developed, which assumes the dynamics of the group to be a multivariate Ornstein–Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders’ state. Sequential Monte Carlo approaches, and specifically the sequential Markov chain Monte Carlo approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao–Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group.

中文翻译：

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使用贝叶斯蒙特卡罗方法的连续动态领导力推断

等级制度和领导互动通常发生在动物群体、人群和车辆运动中。与其他人相比，此类交互通常受到一个或多个拥有关键领域信息（例如，最终目的地、环境限制和最佳路线）或相关特征（例如，更好的导航、感知和决策能力）的个人的影响。团体。本文提出了一个框架，用于从跟踪群体的嘈杂感官观察中自动识别群体结构和领导力。因此，开发了一个新的领导者-追随者模型，该模型假设群体的动态是一个多元的 Ornstein-Uhlenbeck 过程，指定的领导者漂流到目的地，追随者恢复到领导者的状态。顺序蒙特卡罗方法，尤其是顺序马尔可夫链蒙特卡洛方法，被用来从概率上推断出不断发展的领导结构。采用 Rao-Blackwellisation 方案，通过卡尔曼滤波以封闭形式推断组中对象的运动状态。实验表明，所提出的技术可以通过直接考虑团队的领导互动来成功确定具有挑战性的场景中的领导结构，并相应提高跟踪精度。