title: Ð¡Ð¸ÑÑÐµÐ¼Ð° ÑÑÐµÐºÐ¸Ð½Ð³Ð° Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¸ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð¿Ð¾ Ð²Ð¸Ð´ÐµÐ¾Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð½Ð° embedded Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ðµ
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: Human position tracking system by video image on the embedded platform
Creators: name: Ð¡ÑÐ°ÑÐ¸ÐµÐ², 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: 2021
Subjects: ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°
Ð²ÑÑÑÐ°Ð¸Ð²Ð°ÐµÐ¼ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
python
tensorflow
convolutional neural networks
image recognition
optimization of neural networks
object tracking
human tracking
embedded systems

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸ Ñ ÐºÐ°Ð¼ÐµÑÑ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð·Ð°Ð¿ÑÑÐºÐ° Ð½Ð° Ð²ÑÑÑÐ°Ð¸Ð²Ð°ÐµÐ¼ÑÑ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ. ÐÐ° Ð±Ð°Ð·Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð° Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ¾Ð¹ SSD MobileNet. Ð¡Ð¾Ð·Ð´Ð°Ð½Ð° ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾ÑÑÐ»ÐµÐ¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð² ÐºÐ°Ð´ÑÐµ Ð¸ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° ÐµÑ ÑÐ°Ð±Ð¾ÑÐ° Ð½Ð° Ð¼Ð¸ÐºÑÐ¾ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐµ Raspberry Pi 4 Model B 4 Gb. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÐµÑÑÑ Ð½Ð° Ð¿ÑÐµÐ´Ð¼ÐµÑ ÑÐ»Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ð¸, ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ ÑÐ°Ð±Ð¾ÑÑ (ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ ÐºÐ°Ð´ÑÐ¾Ð² Ð² ÑÐµÐºÑÐ½Ð´Ñ), Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¼Ð¾ÑÐ½Ð¾ÑÑÐµÐ¹, Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼ÑÑ Ð´Ð»Ñ Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ. Ð ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ TensorFlow Lite.
description: This paper is devoted to the study of modern neural network architectures for solving the problem of detecting objects in real time in the image from a camera using neural networks. A comparative review of methods for optimizing neural networks for embedded systems was carried out as part of the research. Based on existing methods, a neural network with SSD MobileNet architecture was trained and optimized. A system for tracking the position of a person in the frame has been created and its work has been demonstrated on a Raspberry Pi 4 Model B 4GB microcomputer. The resulting model is compared for the complexity of integration, speed of work (number of frames per second), computing power required for their work. In the development of the application, the components of the TensorFlow Lite library were used.