Maritime Targets Detection from Ground Cameras Exploiting Semi-supervised Machine Learning

Abstract

This paper presents a vision-based system for maritime surveillance, using moving PTZ cameras. The proposed methodology fuses a visual attention method that exploits low-level image features appropriately selected for maritime environment, with appropriate tracker. Such features require no assumptions about environmental nor visual conditions. The offline initialization is based on large graph semi-supervised technique in order to minimize user’s effort. System’s performance was evaluated with videos from cameras placed at Limassol port and Venetian port of Chania. Results suggest high detection ability, despite dynamically changing visual conditions and different kinds of vessels, all in real time.