Embedded vision systems for ship recognition

Maritime security includes reliable identification of ship entering and leaving a nation´s territorial waters. Automated systems that could identify a ship could complement existing electronic signal identification methods. The use of Forward Looking Infrared (FLIR) and Synthetic Aperture Radar (SAR) enables ship image acquisition round-the-clock but their cost and complexity means few installations are available. The use of lower cost embedded vision systems using visible light for surveillance in a low-bandwidth sensor network could complement existing surveillance methods to improve surveillance coverage. This paper presents an overview of automatic ship detection methods with a view towards embedded implementation of suitable algorithms on optical smart cameras. We present results on applying Hu´s moment invariants for feature extraction on several classification algorithms. We achieved accuracies of close to 80% using the KStar and multilayer perceptron classifiers in recognizing one of four ship classes.