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In our current research, we propose a ‘maritime detection framework 2.0’, in which multi-platform sensors are combined with detection algorithms. In this paper, we present a comparison of detection algorithms for EO sensors within our developed framework and quantify the performance of this framework on representative data.
Automatic detection can be performed within the proposed framework in three ways: 1) using existing detectors, such as detectors based on movement or local intensities; 2) using a newly developed detector based on saliency on the scene level; and 3) using a state-of-the-art deep learning method. After detection, false alarms are suppressed using consecutive tracking approaches. The performance of these detection methods is compared by evaluating the detection probability versus the false alarm rate for realistic multi-sensor data.
New types of maritime targets require new target detection strategies. Combining new detection strategies with existing tracking technologies shows potential increase in detection performance of the complete framework.
Trackers make errors, for example, due to inaccuracies in detection, or motion that is not modeled correctly. Instead of improving this tracking using the limited information available from a single measurement, we propose a method where tracks are merged at a later stage, using information over a small interval. This merging is based on spatiotemporal matching. To limit incorrect connections, unlikely connections are identified and excluded. For this we propose two different approaches: spatiotemporal cost functions are used to exclude connections with unlikely motion and appearance cost functions are used to exclude connecting tracks of dissimilar objects. Next to this, spatiotemporal cost functions are also used to select tracks for merging. For the appearance filtering we investigated different descriptive features and developed a method for indicating similarity between tracks. This method handles variations in features due to noisy detections and changes in appearance.
We tested this method on real data with nine different targets. It is shown that track merging results in a significant reduction in number of tracks per ship. With our method we significantly reduce incorrect track merges that would occur using naïve merging functions.
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