As our AIS data set didn’t provide any information any information regarding “Ground Truth”, we followed the strategy mentioned in Section 8.1 for detecting ground truth. User has to provide inputs as shown in Fig. 9.4 and click the button Detect Ground Truth for executing the ground truth detection process. During the execution process system will pro-vide visualization in Google Earth for the track segments to user for interactively selecting the VoyageIDs of ground truth.
Figure 9.4: Input Selection for Ground Truth Detection
Chapter 10
Conclusion
A major challenge in automated anomalous vessel detection in the maritime domain is the high rate of false alarms (due to the use of mainly kinematic information). Existing knowledge driven approaches address this problem by incorporating domain knowledge in the form of pre-defined rules. Since rules are largely static, they do not capture the scenarios of individual vessels, such as the weather and sea conditions at the time and location of a potential anomaly. In this thesis, we proposed contextual verification that incorporates contextual information to filter false alarms. “Contextual information” refers to any factors that potentially impact a vessel’s behaviour (e.g., weather and sea conditions) and is specific to the location and time of a vessel. Empirical studies using an AIS data set obtained from the U.S. Coast Guard suggest the potential of this approach in reducing false alarms. This approach can easily adapt to new contextual information.
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