Recognizing the formations of CVBG based on multiviewpoint context

Recently, context-based recognition approaches have gained more and more attention. However, conventional algorithms usually do not have a satisfactory performance when recognizing formations of a carrier battle group (CVBG), due to the complexity of sea backgrounds and the context of ships. Detailed information about each enemy ship cannot be obtained because monitoring points are usually far from CVBG areas, and a CVBG is assumed to be formed by some scattered points that cannot be described by a fixed shape. But most current algorithms are proposed to recognize continuous objects, such as handwriting letter, face, etc. In order to recognize CVBG formation characterized by a set of scattered points, we propose a novel approach called multiviewpoint context (MVC). Formation recognition needs to deal with invariance problems in scale and rotation because monitoring points come from random heights and directions. We address those problems by calculating contextual information in natural-coordinate systems, which are established on viewpoints selected from the Archimedes spiral. We can also obtain sufficient information about a CVBG from a series of viewpoints. It is difficult to get the local information of a viewpoint because the local region of each viewpoint is not easy to define for scattered points, and a probability density function (pdf) is introduced to describe the local information of viewpoints in the standard formation. The similarity between two formations is measured by combining the MVC descriptor and the pdf. We present a self-adaptive method to identify formation by utilizing the similarities between all pairs of standard formations. The experimental results demonstrate that our algorithm outperforms the current state-of-the-art methods in formation recognition.