Classification of small boats in infrared images for maritime surveillance

Autonomous round-the-clock observation of wide critical maritime areas can be a powerful support for border protection agencies to avoid criminal acts like illegal immigration, piracy or drug trafficking. These criminal acts are often accomplished by using small boats to decrease the probability of being uncovered. In this paper, we present an image exploitation approach to detect and classify maritime objects in infrared image sequences recorded from an autonomous platform. We focus on high robustness and generality with respect to variations of boat appearance, image quality, and environmental condition. A fusion of three different detection algorithms is performed to create reliable alarm hypotheses. In the following, a set of well-investigated features is extracted from the alarm hypotheses and evaluated using a two-stage-classification with support vector machines (SVMs) in order to distinguish between three object classes: clutter, irrelevant objects and suspicious boats. On the given image data we achieve a rate of 97% correct classifications.