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.

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元级和对抗性跟踪特别部分的前言

本专题部分的九篇论文侧重于元级和对抗性跟踪。大量完善的跟踪算法旨在根据可用的感官观察，随时间估计单个或多个目标的潜在运动学状态（例如，位置、速度、高阶运动学或任何其他时空特征），包括来自几个来源。在这里，我们将此类技术称为传感器级跟踪器。元级和对抗性跟踪呈现了一种从场景的传统观点转变，即对象以未经预谋的方式彼此独立移动且不考虑可能的竞争或群体结构，转向综合观点，其中意图、异常、群体互动、并且可以自动学习竞争对手/对手的特征。这还可以通过利用推断的元级信息实现更准确的状态估计。此处包含的论文展示了一系列最新的相关技术发展和应用。它包括九篇精选文章，借鉴了随机建模、计算方法、统计过滤、传感系统等方面的最新进展。