This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.
Video surveillance on both marine and inland waters still only plays a mainly auxiliary role in vessel traffic observation and management. The newest technical achievements in visual systems allow camera images to be used in more sophisticated tasks, such as automatic vessel recognition and identification in observed areas. With the use of deep learning algorithms and other artificial intelligence methods, such as rough sets and fuzzy sets, new functions can be designed and implemented in monitoring systems. In this paper the challenges that were encountered and the technology that has been developed in managing video streams are presented as well as the images needed for tests and proper operation of the designed Ship Recognition and Identification System (SHREC). The current technologies, typical setups and capabilities of cameras, with regard to existing on-water video monitoring systems, are also presented. The aspects of collecting the test data in the Szczecin Water Junction area are also described. The main part of the article focuses on presenting the video data pre-processing, storing and managing procedures that have been developed for the purposes of the SHREC system.
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