evidence combination rules (ECRs) [16], probability box [17], and ER rule. The ER rule does not need the prior distribution about patterns or states, as it constitute a likelihood modelling process. Therefore, this research aims to propose an intelligent approach to extract moving vessels from blips on the basis of the ER rule. A proposed approach 3.1 Step 1: The quantifications of inter-frame differences In fact, approximate dynamic information indicated by inter-frame differences is sufficient for manual identification. The problem is that when there are too many vessels, such a manual inspection becomes impractical. Hence, an intelligent approach of simulating the manual work will be helpful for navigational and maritime safety. Experienced operators are able to achieve a high identification accuracy under uncertainties, because they know the regularities of moving vessels after a long term observation. For instance, the speed of a moving vessel in a specific waterway is generally stable. Therefore, a velocity indicated by a blip is a piece of direct evidence for authenticity identification. Without any filtering algorithm, it is possible to estimate the authenticity probability of a blip based on its velocity in adjacent frames. Moreover, operators can take other attributes of a blip into consideration in order to make comprehensive and accurate identification. Therefore, it is required to quantify these attributes. In fact, many attributes of a blip can be taken as evidence for identification, such as velocity, course, size, colour, width, and length. However, there is a condition of using the ER rule that multiple pieces of evidence should be independent from each other. Hence, three types of evidence or attributes which are considered to be independent of each other in terms of their contributions to moving vessel identification are selected, namely, velocity, motion direction (i.e., course), and blip shape. The velocity and motion direction can be easily understood. Real moving vessels are more likely to move with a steady velocity and a steerable course, and noise objects are more likely to drift around a
evidence combination rules (ECRs) [16], probability box [17], and ER rule. The ER rule does not need the prior distribution about patterns or states, as it constitute a likelihood modelling process. Therefore, this research aims to propose an intelligent approach to extract moving vessels from blips on the basis of the ER rule. A proposed approach 3.1 Step 1: The quantifications of inter-frame differences In fact, approximate dynamic information indicated by inter-frame differences is sufficient for manual identification. The problem is that when there are too many vessels, such a manual inspection becomes impractical. Hence, an intelligent approach of simulating the manual work will be helpful for navigational and maritime safety. Experienced operators are able to achieve a high identification accuracy under uncertainties, because they know the regularities of moving vessels after a long term observation. For instance, the speed of a moving vessel in a specific waterway is generally stable. Therefore, a velocity indicated by a blip is a piece of direct evidence for authenticity identification. Without any filtering algorithm, it is possible to estimate the authenticity probability of a blip based on its velocity in adjacent frames. Moreover, operators can take other attributes of a blip into consideration in order to make comprehensive and accurate identification. Therefore, it is required to quantify these attributes. In fact, many attributes of a blip can be taken as evidence for identification, such as velocity, course, size, colour, width, and length. However, there is a condition of using the ER rule that multiple pieces of evidence should be independent from each other. Hence, three types of evidence or attributes which are considered to be independent of each other in terms of their contributions to moving vessel identification are selected, namely, velocity, motion direction (i.e., course), and blip shape. The velocity and motion direction can be easily understood. Real moving vessels are more likely to move with a steady velocity and a steerable course, and noise objects are more likely to drift around a