Abstract
The interest in video surveillance has been increasing in the fields of maritime industry in the past decade. Maritime transportation system is a vital part of the world’s economy and the extent of global ship traffic is increasing. This trend encourages the development of intelligent surveillance systems in the maritime zone. The development of intelligent surveillance systems includes sensor and data fusion, which incorporates multispectral and multisensory data to replace the traditional approach with radars only. Video cameras are widely used since they capture images of greater resolution than most sensor systems. Also, combined with video analytics they provide sensors with high capability, complex pattern recognition analytics, and multiple variables for the decision making process. In this paper, an overview of a small part of the system is presented – horizon detection.
Highlights
Since radar tracking is sensitive to shape, size and material of the targets, it has to be enriched with other types of sensors for better situational awareness, collision avoidance, and navigation
The projection-based, region-based, hybrid and artificial neural networks (ANN)-based methods for horizon detection are discussed in the paper
Simplicity is the main advantage of projection-based methods, but they often fail in the horizon line detection when horizon is not the dominant line in the frame
Summary
Since radar tracking is sensitive to shape, size and material of the targets, it has to be enriched with other types of sensors for better situational awareness, collision avoidance, and navigation. As described in Prasad et al (2017), basic maritime video surveillance system is composed of five main components: the initial detector, image processor, classifier, tracker, and behaviour analyser if necessary. Horizon information is used in some object detection approaches and for the reduction of false positives for a given object detection rate (Jeong et al, 2018a) It is used for distance prediction of another object to the camera (Gladstone et al, 2016) or for maritime target detection and tracking in infrared images (Jian and Wen, 2019). The second section describes the main topic, using math and examples It is divided into several subsections dealing with projection-based, region-based, hybrid and ANN (Artificial Neural Networks) methods.
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