A Method of Air Object Recognition Based on the Normalized Contour Descriptors and A Complex-valued Neural Network

This paper reports a study into the methods for recognizing the type of an air object on a digital image acquired from an air situation video monitoring system. A method has been proposed that is based on the application of a specific neural network, which solves the problem of categorizing multidimensional complex vectors of objects' features based on complex calculations. In this case, a feature vector for recognizing the type of an air object is built on the basis of a Fourier transform for the sequence of coordinates of its two-dimensional contour. A technique has been proposed to train a neural network to recognize the type of an air object based on three image classes corresponding to three projections. This makes it easier to solve the classification problem owing to a more compact arrangement of the multidimensional feature vectors. The architecture of an air situation video monitoring system has been suggested, which includes an image preprocessing module and a module of a complex-valued neural network. Pre-processing makes it possible to identify an object's contour and build a sequence of normalized descriptors, which are partially independent of the spatial position of the object and the contour processing technique. Existing methods of air object recognition require significant computational resources and do not take into consideration the specificity of recognizing objects with three degrees of freedom or do not account for the complex nature of the numerical representation of a contour. This study has shown that the reported results make it easier to train a neural network and reduce the hardware requirements in order to solve the task of air situation video monitoring. The proposed solution leads to increased mobility and extends the scope of application of such systems, including individual device