title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¶ÐµÐ»ÐµÐ·Ð½Ð¾Ð´Ð¾ÑÐ¾Ð¶Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð»Ð¾ÑÐ½Ð° Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Railroad recognition in the video stream
Creators: name: ÐÐ°ÑÐ°ÑÐ¾Ð²Ð¸Ñ, ÐÐ¸ÑÐ°Ð¸Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²
ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐ°
semantic segmentation
video stream processing

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description: The subject of the graduate qualification work is Â«Railroad recognition in theÂ video streamÂ». This work is devoted to the study of neural network architectures for segmentation of objects in the video stream. The purpose of this work is to develop a neural network architecture using temporal information from the video stream to improve segmentation of a railroad at the distance of more than 150 meters from the locomotive on which the tracking camera is installed and to write a program for streaming video processing. The basis of the neural network architecture and the railroad recognition method were taken from an article Â«Railroad semantic segmentation on high-resolution imagesÂ» S. Belyaev, 2020. An analysis was made of several architectures of neural networks for a video semantic segmentation, using different methods to obtain information from previous frames. As a result, several neural network architectures have been developed to recognize the railroad in the video stream: with usage of optical flow, with usage of attention propagation modules, with usage of memory module. This paper describes the architectures of neural networks and compares the accuracy of the described approaches with the segmentation of each frame, regardless of the previous ones. According to the results of the work carried out, one can estimate the effectiveness of the methods of using temporal information in a comparison with naive application of an image semantic segmentation model in a frame-by-frame manner.