АЛГОРИТМ ПРЕДВАРИТЕЛЬНОЙ ОБРАБОТКИ ВИДЕОИЗОБРАЖЕНИЙ ДЛЯ ПОВЫШЕНИЯ ТОЧНОСТИ ОБНАРУЖЕНИЯ МАЛОРАЗМЕРНЫХ ОБРАЗОВ

Abstract

Recognition of certain patterns in video images captured by a camera is carried out usingtraining methods based on convolutional neural networks. The larger the number of images withmultiple features and the more diverse the training sample of video images, the better the convolutionalneural networks extract features from the sequence of video images that were not included inthe training sample. This is a consequence of increasing the accuracy of detecting visual images onvideo images containing features of target images. However, there are limitations in improving thedetection performance when the size of the image to be detected is much smaller than the backgroundarea, or when the image is described with little information. To solve problems of this kind, the authorsof the article have developed an algorithm for the spatio-temporal integration of informationabout the movement of dynamic images. The algorithm processes a fixed number of video images atcertain points in time and extracts new independent signs of motion of dynamic images based onspace-time processing of video images. Further, it combines new local motion features with the originalvideo image features. This allows you to add a motion feature of dynamic images while preservingthe original image features that describe static images. Areas of the video image that characterizethe motion feature are displayed in a «color» cluster. The use of pre-processing is aimed at improving the accuracy of pattern detection, provided there are dynamic visual images on a static background.If the camera is in scan mode, a static background can be provided with a video stabilizer.Experimentally, estimates of integral criteria for the accuracy of detection neural network algorithmshave been obtained, showing an increase in the accuracy of detecting visual images usingthe algorithm for spatial-temporal integration of motion information.