Multi-Sensor Fusion-Driven Surface Vessel Identification and Tracking Using Unmanned Aerial Vehicles for Maritime Surveillance

The rapidly-developing artificial intelligence and edge computing technologies have been actively promoting the evolution of autonomous vehicles. The flexible and powerful unmanned aerial vehicles (UAVs) have become suitable application platforms for maritime surveillance. In the maritime Internet of Things (IoT), both airborne cameras and automatic identification system (AIS), which, respectively, provide visual and positioning data, have become the frequently-used and cost-effective sensors. It becomes necessary to fuse the AIS and visual data to detect the visual appearances of surface vessels with obtaining the abundant information on position, movements, and identity, etc. In this work, we propose to develop a multi-sensor fusion-driven computational method (termed MSF-VIT) for highly-reliable surface vessel identification and tracking under different conditions. In particular, it mainly consists of two components, i.e., multi-sensor data fusion, and vessel identification and tracking. In particular, the multi-sensor data fusion method is, firstly, presented to process multi-source heterogeneous data and match the visual and AIS targets. We then propose to improve the traditional SORT algorithm to achieve more accurate vessel tracking. Extensive experiments have been implemented on the edge computing device to evaluate the feasibility and robustness of MSF-VIT. Moreover, it is also proven that our method is capable of promoting real-time maritime surveillance and supporting the privacy-preserving of vessel information.