OBJECTS DETECTION BASED ON THE SEA SURFACE VIDEO FRAGMENTS CROSS-CORRELATION

Abstract

The work is devoted to the development of a method for the objects detecting on an agitated sea surface video based on the analysis of the differences in the variability of the object and the sea surface image fragments on the neighboring frames. The proposed method does not use data about the object size, its shape, brightness, etc. The decision function has been developed that can be used to estimate the variability of a given frames fragment, based on the normalized cross-correlation coefficients values of the corresponding fragments on a video subsequent frames. The decision rule has been developed based on the proposed decision function, in which we use the threshold value (the critical domain boundary) determined at the training stage when analyzing the frames sequence fragments containing only the agitated sea surface image. The efficiency of the developed objects detection method on the agitated sea surface is demonstrated based on computational experiments. The values of the decision function critical domain boundary obtained at the training stage and the corresponding values of the type II error probability at the detection stage are given. The presented computational experiments results demonstrate that the developed method makes it possible to detect the object on video frames with the type II error probability equal to zero.