A scheme cross trajectories, vessel attributes and the movement context for detecting rare behaviors through preprocessing, kNN-based clustering, and verification.
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Only extremely few anomalies are useful which are rare behaviors.
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The interactive detection process requires an instant response that is a big challenge for.
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The more similar trajectories gather in an Area of Interest, the less probability of anomalies they are.
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The small reachability distance values have limited effect on lrd.
Abstract
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.