Institutul de Matematică şi Informatică "Vladimir Andrunachievici"
Universitatea de Stat din MoldovaRO EN
Integrating Recurrent Neural Networks and Loss Function Optimization for Efficient Indoor Camera Positioning
Authors: Shamsul Alam, Farhan Mohamed, Bellal Hossain
Keywords: Camera positioning, Indoor navigation, Pose estimation, Pose loss, Recurrent neural network.
– 0.87 Mb
Keywords: Camera positioning, Indoor navigation, Pose estimation, Pose loss, Recurrent neural network.
Abstract
Camera position is essential for many applications, such as monitoring, tracking, and recognizing individuals. This study proposed an integrated design that combines recurrent neural networks (RNNs) and a loss function modification approach to improve the accuracy of indoor camera location. RNNs enable the system to generate accurate estimations based on previous information by extracting temporal dependencies and patterns from the camera information. We optimized the loss function to enhance the indoor camera position's overall performance and convergence speed. This combination technique allows the proposed method to considerably increase the accuracy of camera location prediction in indoor conditions. We validated the effectiveness of the proposed approach and demonstrated its improved accuracy and robustness through extensive evaluation of many indoor datasets. The results show that our combined approach outperforms existing methods and has enormous potential for real-world applications in indoor activity recognition, navigation optimization systems, and safety surveillance.Shamsul Alam
ORCID: https://orcid.org/0000-0002-9419-3928
Department of Emergent Computing, Faculty of Computing,
Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
and
Department of Computer Science and Artificial Intelligence,
College of Computing and Information Technology,
University of Bisha, Bisha, 61922, Saudi Arabia
E-mail:
Farhan Mohamed
ORCID: https://orcid.org/0000-0002-5298-8642
Department of Emergent Computing, Faculty of Computing,
Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
and
Media and Game Innovation Centre of Excellence (MaGICX),
University Technology Malaysia, Johor Bahru, 81310, Malaysia
E-mail:
Bellal Hossain
ORCID: https://orcid.org/0000-0003-3877-7037
Department of Computer Science, Faculty of Computing,
Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
and
Department of Information Systems and Cyber Security,
College of Computing and Information Technology,
University of Bisha, Bisha, 61922, Saudi Arabia
E-mail:
ORCID: https://orcid.org/0000-0002-9419-3928
Department of Emergent Computing, Faculty of Computing,
Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
and
Department of Computer Science and Artificial Intelligence,
College of Computing and Information Technology,
University of Bisha, Bisha, 61922, Saudi Arabia
E-mail:
Farhan Mohamed
ORCID: https://orcid.org/0000-0002-5298-8642
Department of Emergent Computing, Faculty of Computing,
Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
and
Media and Game Innovation Centre of Excellence (MaGICX),
University Technology Malaysia, Johor Bahru, 81310, Malaysia
E-mail:
Bellal Hossain
ORCID: https://orcid.org/0000-0003-3877-7037
Department of Computer Science, Faculty of Computing,
Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
and
Department of Information Systems and Cyber Security,
College of Computing and Information Technology,
University of Bisha, Bisha, 61922, Saudi Arabia
E-mail:
DOI
https://doi.org/10.56415/csjm.v33.04Fulltext
– 0.87 Mb
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