ALGORITHM FOR PRE-PROCESSING VIDEO IMAGES TO INCREASE THE ACCURACY OF SMALL OBJECT DETECTION

Abstract

Recognition of certain patterns in video images captured by a camera is carried out using training methods based on convolutional neural networks. The larger the number of images with multiple features and the more diverse the training sample of video images, the better the convolutional neural networks extract features from the sequence of video images that were not included in the training sample. This is a consequence of increasing the accuracy of detecting visual images on video images containing features of target images. However, there are limitations in improving the detection performance when the size of the image to be detected is much smaller than the background area, or when the image is described with little information. To solve problems of this kind, the authors of the article have developed an algorithm for the spatio-temporal integration of information about the movement of dynamic images. The algorithm processes a fixed number of video images at certain points in time and extracts new independent signs of motion of dynamic images based on space-time processing of video images. Further, it combines new local motion features with the original video image features. This allows you to add a motion feature of dynamic images while preserving the original image features that describe static images. Areas of the video image that characterize the 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 algorithms have been obtained, showing an increase in the accuracy of detecting visual images using the algorithm for spatial-temporal integration of motion information.