Abstract
On the issue of low precision of ship anomaly behavior detection method based on global variable and calculation complexity of ship anomaly detection based on local variable, a combination of K Nearest Neighbor (KNN) and Local Outlier Factor (LOF) algorithm for ship anomaly behavior detection is proposed in this paper. Firstly, ship anomaly data candidate set is filtered by K nearest neighbor, then calculating local deviation index by LOF algorithm, lastly setting threshold value to judge ship anomaly behavior, so as to achieve rapid, effective ship anomaly behavior detection. To a certain extent, it helps the maritime safety supervision department to identify the potential risks of their ship, and improve regulatory efficiency.
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