A variety of anomaly detection algorithms have been applied to surveillance tasks for detecting threats with some success. However, it is not clear which anomaly detection algorithms should be used for domains such as ground-based maritime video surveillance. For example, recently introduced algorithms that use local density techniques have performed well for some tasks, but they have not been applied to ground-based maritime video surveillance. Also, the reasons for the performance differences of anomaly detection algorithms on problems of varying difficulty are not well understood. We address these two issues by comparing families of global and local anomaly detection algorithms on tracks extracted from ground-based maritime surveillance videos. Obtaining maritime anomaly data can be difficult or even impractical. Therefore, we use a generative approach to vary and control the difficulty of anomaly detection tasks and to focus on borderline and difficult situations in our empirical comparison studies. We report that global algorithms outperform local algorithms when tracks have large and unstructured variations, while they perform equally well when the tracks have only minor variations
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