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Foreword to the Special Section on Meta-Level and Adversarial Tracking

机译：前言至荟萃级和对抗性跟踪的特殊部分

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摘要

The nine papers in this special section focus on meta-level and adversarial tracking. A plethora of well-established tracking algorithms aim to estimate, over time, the latent kinematic state (e.g., position, velocity, higher order kinematics, or any other spatiotemporal characteristic) of a single or multiple targets based on the available sensory observations, including from several sources. Here, we refer to such techniques as sensor-level trackers. Meta-level and adversarial tracking presents a shift away from the traditional viewpoint of a scene where objects move independently of one another in an unpremeditated manner and without regard to possible competition or group structures, toward an integrated viewpoint where intents, anomalies, group interactions, and characteristics of competitors/adversaries can be automatically learned. This also enables more accurate state estimation by capitalizing on inferred meta-level information. The papers included here showcase a diverse set of recent relevant technical developments and applications. It comprises of nine selected articles, drawing on recent advances in stochastic modeling, computational methods, statistical filtering, sensing systems, and others.
机译：这篇特殊部分中的九篇论文侧重于荟萃级和对抗性跟踪。一种良好的良好的跟踪算法旨在基于可用的感官观察来估计单个或多个目标的潜在运动状态（例如，位置，速度，高阶动力学或任何其他时空运动学），包括来自几个来源。在这里，我们将这些技术称为传感器级跟踪器。元级和普发的追踪呈现出现场的传统观点的转变，其中物体以不纯粹的方式独立于彼此独立，而不考虑可能的竞争或组结构，朝着意图，异常，群体互动，可以自动学习竞争对手/对手的特征。这也通过在推断的元级信息上大写了，实现了更准确的状态估计。这里包含的论文展示了各种各样的最近相关技术开发和应用程序。它包括九种所选文章，借鉴随机建模，计算方法，统计滤波，传感系统等的最近进步。

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