Rare behaviors indicate important events and situations in maritime surveillance applications. State-of-the-art methods provide many effective solutions to detect anomalous behaviors. Meanwhile, most solutions are parameter-laden and too costly to identify useful rare behaviors with human knowledge in a visual analytics manner. This paper is concerned with a scheme cross trajectories, vessel attributes and the movement context for detecting rare behaviors through preprocessing, kNN-based clustering, and verification. Although the scheme involves several parameters, we demonstrate that they are able to be tackled in thresholds. As a result, a rare behavior factor is the single parameter that affect the detecting results. The proposed scheme is evaluated via a simulated data set for performance and a real life AIS data for effectiveness. Results show that high accuracy to labelled anomalies and useful rare behaviors can be achieved.

稀有行为表明海上监视应用中有重要事件和情况。最新的方法为检测异常行为提供了许多有效的解决方案。同时，大多数解决方案都带有参数，而且成本高昂，无法以可视化分析方式用人类知识识别有用的罕见行为。本文涉及用于通过预处理，基于kNN的聚类和验证来检测稀有行为的方案跨轨迹，船只属性和运动上下文。尽管该方案涉及多个参数，但我们证明了可以在阈值中解决它们。结果，罕见的行为因素是影响检测结果的单个参数。通过模拟数据集评估性能，并通过现实生活中的AIS数据评估有效性。