Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons, and others. Due to the unique characteristics of the maritime environment, the location of even inert objects changes through time, depending on the weather conditions, water currents, etc. Unmanned aerial vehicles (UAVs) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained, and used to automatically detect and track objects of specific types and characteristics. EFFECTOR is an EU-funded project, developing an Interoperability Framework for maritime surveillance. Within the project, we developed an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. Using the on-board computation unit of the UAV, we ran and present the results of a series of comparative tests among possible architecture sizes and training datasets for the detection and tracking of objects in the maritime environment. We tested architectures based on their efficiency, accuracy, and speed. A combined solution for training the datasets is suggested, providing optimal efficiency and accuracy.

Unusual pattern extraction and crowd dynamics and analysis has been receiving a constantly growing attention in the recent years contributing to safety, crisis management and situational awareness applications. Traditional video-based systems in this field that are exclusively monitored by human agents, are error-prone and present limited human performance. In this context, the design and implementation of an integrated tethered Unmanned Aircraft System (tUAS), namely INtelligentUxV Surveillance-Unusual Pattern Extraction (INUS-UPE) platform for crowd dynamics and unusual pattern extraction is presented. The INUS-UPE platform adopts a combination of key features and state-of-theart technologies such real-time processing in the edge, videobased pattern analysis for object detection, multiple object tracking, and unusual pattern extraction, web-based user interface (UI) for visualization and possibility of information sharing related the extracted events to external systems such common operational pictures (COPs), Command & Control (C2) platforms, and Human Machine Interfaces (HMIs).