We address the problem of automatic detection and labelling of far-field activity patterns from target trajectories using vector field abstractions that are estimated by multiple communicating agents in a Kalman filter (KF) framework. We propose a novel 2D application of the diffusion KF. The proposed approach yields multiple vector field abstractions at the same spatial regions by allowing the estimator agents to create a library of states. We compute internal consistency measures to assess the estimated vector fields, and establish thresholds that signal low performance estimates. Experimental results on synthetic and real data sets show that the proposed approach correctly detects activity patterns from training trajectories and, using new test trajectories one at a time, either matches them to previously detected activity patterns or detects new activity patterns that are added to the agents library. Moreover, the internal consistency measures correctly flag the low-performer vector field abstractions.

中文翻译：

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活动模式的多代理检测和标记

我们使用由卡尔曼滤波器 (KF) 框架中的多个通信代理估计的矢量场抽象来解决从目标轨迹自动检测和标记远场活动模式的问题。我们提出了扩散 KF 的一种新颖的 2D 应用。通过允许估计代理创建状态库，所提出的方法在相同的空间区域产生多个向量场抽象。我们计算内部一致性度量以评估估计的向量场，并建立表示低性能估计的阈值。在合成和真实数据集上的实验结果表明，所提出的方法可以正确地从训练轨迹中检测活动模式，并且一次使用一个新的测试轨迹，要么将它们与之前检测到的活动模式相匹配，要么检测添加到代理库中的新活动模式。此外，内部一致性度量正确标记了低性能向量场抽象。