Location estimation in a maritime environment using a monocular camera

Abstract

Maritime surveillance is a very important task in coastal areas, especially in harbour environments. The most popular such systems include components like Automatic Identification System (AIS) and Radar. Camera based visual surveillance can be used as an alternative to these systems in order to overcome the lacking features of them. Sea surface object detection and identification is a major need for such visual surveillance systems. Most of the current visual surveillance systems don't have the ability of identifying vessels in real time. A vessel can be identified using information from other systems, if the location of the vessel is identified. Location estimation of sea surface objects is mainly explored in this research. Video stream from a single geo stationary camera is used as the input; however camera properties are not used for any calculation. Mainly two distance measurements are considered and different approaches for estimating the distances are explored. Neural network approach gave considerably accurate results in vertical distance estimation and it was found that the shortest distance from camera to the object can be measured best using B-spline 3D curve fitting. Data taken from AIS is used for fitting curves and training neural network. After calculating distances, latitudes and longitudes are calculated. An evaluation has been done comparing the calculated values and the values obtained from AIS data using various statistical tests. There, the different approaches are compared and accuracy levels are described. Vessel identification is done comparing the estimated location and the available location information from AIS data.