Improvements in ship tracking in electro-optical and infrared data using appearance

Naval ships have camera systems available to assist in performing their operational tasks. Some include automatic detection and tracking, assisting an operator by keeping a ship in view or by keeping collected information about ships. Tracking errors limit the use of camera information. When keeping a ship in view, an operator has to re-Target a tracked ship if it is no longer automatically followed due to a track break, or if it is out of view. When following several ships, track errors require the operator to re-label objects. 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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
The Society of Photo-Optical Instrumentation Engineers (SPIE)