Issue |
E3S Web Conf.
Volume 474, 2024
X International Annual Conference “Industrial Technologies and Engineering” (ICITE 2023)
|
|
---|---|---|
Article Number | 02022 | |
Number of page(s) | 9 | |
Section | Applied IT Technologies in Energy and Industry | |
DOI | https://doi.org/10.1051/e3sconf/202447402022 | |
Published online | 08 January 2024 |
Predicting of a person's position in trajectory tracking from a continuous video stream
V. A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
* Corresponding author: osa18@yandex.ru
The paper proposes a method for predicting when a person enters a forbidden zone during his trajectory following a video stream, considering individual body parts. The authors used the PP-TinyPose PaddleHub neural network model with its implementation based on two deep neural networks to detect key points of the human body. The paper considers an example of human position prediction from a continuous video stream in indoor trajectory tracking. The authors predicted each key point in the coordinate space of the video stream using a recurrent deep neural network algorithm.
© The Authors, published by EDP Sciences, 2024
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