title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¼ÑÐ·ÐµÐ¹Ð½ÑÑ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ¾Ð² Ð² ÑÐ¸ÑÑÐµÐ¼Ðµ Ð²Ð¸Ð´ÐµÐ¾Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Recognition of museum items in a video surveillance system using a pretrained neural network
Creators: name: ÐÐ¸Ð½Ð¸Ð½, ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
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: 2023
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
ÑÑÐ°Ð½ÑÑÐµÑÐ½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð¿ÐµÑÐµÐºÑÑÑÐ¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
neural networks
object recognition
transfer learning
data augmentation
object overlapping

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ âÂ  Ð£ÑÑÐ°Ð½Ð¾Ð²Ð¸ÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ¾Ð² (Ð¼ÑÐ·ÐµÐ¹Ð½ÑÑ ÑÐºÑÐ¿Ð¾Ð½Ð°ÑÐ¾Ð²) Ð½Ð° ÐºÐ°Ð´ÑÐ°Ñ Ñ ÑÐµÐ»ÐµÐºÐ°Ð¼ÐµÑ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾ÑÑÐ°Ð½Ð½Ð¾Ð³Ð¾ ÑÐµÐ»ÐµÐ²Ð¸Ð´ÐµÐ½Ð¸Ñ Ð¤Ð¾Ð½Ð´Ð¾ÑÑÐ°Ð½Ð¸Ð»Ð¸ÑÐ° ÐÐ¾ÑÑÐ´Ð°ÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð­ÑÐ¼Ð¸ÑÐ°Ð¶Ð° Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ¾Ð´Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¸Ðº Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÐ½Ð°Ð»Ð¸Ð· Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð»ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¾ÑÐºÑÑÑÑÑ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÑÑÑÐ¾Ð² Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. ÐÐ»Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¸ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ SqueezeNet. ÐÐ²Ð¸Ð´Ñ Ð¼Ð°Ð»Ð¾Ð³Ð¾ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð²ÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÑÐ°Ð½ÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð±ÑÐ» Ð²ÑÐ±ÑÐ°Ð½ Ð¼ÐµÑÐ¾Ð´ ÑÑÐ¾ÑÐ°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ ÑÐ¿ÑÑÐºÐ° Ñ Ð¸Ð¼Ð¿ÑÐ»ÑÑÐ¾Ð¼ Â«SGDMÂ». ÐÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð²ÑÐ¾Ð´Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, ÑÑÐ°ÑÑÐ²ÑÑÑÐ¸Ñ Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÑÐ» ÑÐ¾Ð·Ð´Ð°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ SqueezeNet ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¼ÑÐ·ÐµÐ¹Ð½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° ÑÐµÑÑÐ¸ÑÐ¾ÑÐ¸Ð¸ Ð¤Ð¾Ð½Ð´Ð¾ÑÑÐ°Ð½Ð¸Ð»Ð¸ÑÐ° ÐÐ¾ÑÑÐ´Ð°ÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð­ÑÐ¼Ð¸ÑÐ°Ð¶Ð°. ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÐ¼Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð»Ð¾ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Matlab. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÐ¼Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð±ÑÐ»Ð° Ð°Ð´Ð°Ð¿ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¼ÑÐ·ÐµÐ¹Ð½ÑÑ ÑÐºÑÐ¿Ð¾Ð½Ð°ÑÐ¾Ð² Ð¤Ð¾Ð½Ð´Ð¾ÑÑÐ°Ð½Ð¸Ð»Ð¸ÑÐ° ÐÐ¾ÑÑÐ´Ð°ÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð­ÑÐ¼Ð¸ÑÐ°Ð¶Ð°.
description: The given work is devoted to establishing the possibility of recognizing objects (museum exhibits) on frames from television cameras of the State Hermitage CCTV system using pre-trained neural networks. In the course of this work, an analysis of various methods for training neural networks was made. The analysis was carried out using open educational resources and programs for searching and analyzing information. To fulfill the purpose of this study, the SqueezeNet neural network was chosen. Due to the small amount of input data, the transfer learning model was chosen to train the neural network. The stochastic gradient descent method with momentum "SGDM" was chosen as the training method. An algorithm to create additional input images involved in neural network training was developed. An algorithm for training the SqueezeNet neural network to recognize museum objects on the territory of the State Hermitage Repository was created. The studied neural network was trained using the Matlab software. As a result, the studied neural network was adapted to identify museum exhibits of the State Hermitage Repository.