# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 1 Introduction
The problem of detecting SAR missions becomes particularly challenging since the trajectory of a vessel in a mission is decomposed into multiple smaller operations, such as patrolling, rushing to the event, directly collecting people at sea, collecting people by other means such as floating devices or smaller boats, safely returning the collected people to the port and so forth. Recognizing a vessel performing any of those operations is critical into timely understanding incidents, their details and how the complete SAR operation evolves. In our previous work [13], we proposed an unsupervised method for detecting vessels potentially engaged in SAR missions, which was mainly based on the fact that when the vessel is performing a search or a rescue maneuver it is expected to change its course several times within a spatial and temporal distance. In that work, a trajectory simplification algorithm allowed to limit the trajectory points only to those representing major changes in the vessel's course over ground and a density based clustering algorithm allowed to locate the segments of the trajectory that contain many turn points at a close distance (the two parameters of the DBScan density based clustering algorithm, _MinPts_ and _eps_ actually define how many points at least must appear in a small radius around any point in order for it to be considered as core cluster point). In this paper, we revisit the problem of SAR mission identification with an emphasis on the detection of the specific type of maneuvers performed by a vessel. For this reason, we train a classifier that is able to process certain features of the vessel trajectory and distinguish between the different maneuver types. Since it is hard to find and manually annotate lots of SAR trajectory data, we develop a synthetic trajectory generator, which is configured to generate various maneuver patterns and also introduces noise to them in order to simulate real world examples. Using synthetic trajectories only, we train and evaluate our classifier and evaluate it on synthetic and real data. The classifier builds on a shapelet transformation of the original AIS data, which comprise vessels' latitude and longitude for consecutive timestamps and on the extraction of useful features from the resulting time-series data, with the use of a genetic algorithm. The current work focuses on the classification of SAR maneuvers that may happen during a vessel's trip and thus cannot be directly compared to methods that classify the whole voyage as a SAR mission or not (e.g., References [14; 15]) or methods that search for more composite naval events that examine more than one vessels in tandem (e.g., References [1; 2]). However, it proposes a new classification approach for documented maneuvers such as the SAR ones, in the absence of annotated data. The contributions of this work can be summarized as follows:* A method for the classification of composite vessel maneuvers to different types of SAR patterns. The method is supervised but is trained using synthetic data and is based on shapelet features extracted using a genetic algorithm. * A synthetic trajectory generator. * A sampling technique for trajectory data that selects only a few interesting points from a long vessel trajectory, that are still capable of representing the maneuvers contained in it. The following section, highlights some key research works in the field of maritime surveillance and briefly describes the key algorithms that we extend in our work. In Section 3 we give an overview of the proposed framework and in its subsections we provide the details of our work. In Section 4 we illustrate some first results on synthetic and real data and in Section 5, we discuss the next steps of this work.