title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ°Ð´ÑÐ¾Ð² Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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: Application of deep learning methods for the identification of abandoned objects
Creators: name: Ð®ÑÐµÐ², Ð Ð¾Ð¼Ð°Ð½
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: 2024
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÑÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ
ÑÐ¾Ð¿ÑÐ¾Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐµ Ð¾Ð±ÑÐµÐºÑÑ
YOLOV5
YOLOv5
YOLOv8
OpenCV
deep learning
convolutional neural network
detection
tracking
object recognition
abandoned objects

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â Ð¾Ð±ÑÐµÐºÑ Ð½Ð° Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð±ÑÑÑÑÐ¾Ð³Ð¾ Ð¸ Ð½Ð°Ð´ÐµÐ¶Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ¾Ð² ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾Ð³Ð¾ Ðº Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸ÑÐ¼ Ð¾ÑÐ²ÐµÑÐµÐ½Ð½Ð¾ÑÑÐ¸ ÑÑÐµÐ½Ñ Ð¸ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¿ÐµÑÐµÐ´Ð½ÐµÐ¼ Ð¿Ð»Ð°Ð½Ðµ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ ÑÑÐ°Ð¿Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ¾Ð², Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ñ Ð¾ÑÐµÐ½ÐºÐ¾Ð¹ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÐµÐºÑÐ° Ð² ÐºÐ°Ð´ÑÐµ Ð¸ ÐµÐ³Ð¾ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð»ÑÐ´ÐµÐ¹ Ð²Ð¾ÐºÑÑÐ³. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð¼ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ¾Ð² Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð²ÑÑÐ¸ÑÐ°Ð½Ð¸Ñ ÑÐ¾Ð½Ð°. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐµÐ²ÑÑÐ¸Ð»Ð° 91%. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¾ÑÐºÑÑÑÑÐµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÑÑÑÑ Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÑÐµÐ´ÑÑÐ²Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Python. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ Python.
description: The object of research is an object on a video sequence. The aim of the work is to develop a fast and reliable method of detection and identification of left objects. As a result of the research the main stages of searching for left objects were considered, the implementation of the neural network algorithm with estimation of the time of finding the left object in the frame and its position in relation to people around was presented. A comparison with the method of searching for left objects using background subtraction was presented. The accuracy of the presented solution was over 91%. Open educational resources and software for information retrieval and analysis were used. Python automated development tools were used. Python software was used.