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Article

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Title

Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control

Authors

[ 1 ] Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Automatyki i Robotyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee | [ S ] student

Scientific discipline (Law 2.0)

[2.2] Automation, electronics and electrical engineering

Year of publication

2022

Published in

Applied Sciences

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 10

Article type

scientific article

Publication language

english

Keywords
EN
  • neural networks
  • machine learning
  • image processing
Abstract

EN The aim of this work is to present an automatic solution to control the surveillance camera merely by the movements of the operator’s head. The method uses convolutional neural networks that work in a course-to-fine manner to estimate head orientation in image data. First, the image frame of the operator’s head is acquired from the camera on the operator’s side of the system. The exact position of a head, given by its bounding box, is estimated by a Multitask Cascaded Convolutional Network. Second, the customized network for a given scenario is used to classify the orientation of the head-on image data. In particular, the dedicated image dataset was collected for training purposes and was given a discrete set of possible orientations in the vertical and horizontal planes. The accuracy of the estimators is higher than 80%, with an average of 4.12 fps of validation time. Finally, the current head orientation data are converted into a control signal for two degrees of freedom surveillance camera mounting. The feedback response time is 1.5 s, which is sufficient for most real-life surveillance applications.

Date of online publication

23.05.2022

Pages (from - to)

5252-1 - 5252-14

DOI

10.3390/app12105252

URL

https://www.mdpi.com/2076-3417/12/10/5252

Comments

Article Number: 5252

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100

Impact Factor

2,7

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