Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time
The development of mathematical models for detecting moving objects in real-time video streams is a significant task, especially in light of the growing demands for security and process automation. This paper proposes a detection system based on the Faster Region-Based Convolutional Neural Network (...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10737329/ |
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| author | Zhazira Mutalova Anargul Shaushenova Ardak Nurpeisova Maral Ongarbayeva Aidar Ispussinov Sandugash Bekenova Zhanar Altynbekova |
| author_facet | Zhazira Mutalova Anargul Shaushenova Ardak Nurpeisova Maral Ongarbayeva Aidar Ispussinov Sandugash Bekenova Zhanar Altynbekova |
| author_sort | Zhazira Mutalova |
| collection | DOAJ |
| description | The development of mathematical models for detecting moving objects in real-time video streams is a significant task, especially in light of the growing demands for security and process automation. This paper proposes a detection system based on the Faster Region-Based Convolutional Neural Network (Faster R-CNN) model, utilizing various backbone architectures, including Residual Network-50 (ResNet-50), Feature Pyramid Network (FPN), MobileNet Version 3 Large, and Efficient Network-B0 (EfficientNetB0) with a Self-Attention mechanism. The model extracts object features by combining deep neural networks and scaling mechanisms, enabling adaptation to various operating conditions. Experimental results demonstrate that the proposed approach achieves high accuracy, with the MobileNet Version 3 Large model achieving 95.70% accuracy and the Residual Network-50 model reaching 100% accuracy within the first three training cycles (epochs). The Faster Region-Based Convolutional Neural Network model using Efficient Network-B0 with the Self-Attention mechanism reaches 100% accuracy by the third training cycle, maintaining consistent performance throughout the remaining training cycles. The model with the Residual Network-50 and Feature Pyramid Network backbone architecture shows an average loss reduction from 0.0922 in the first training cycle to 0.0102 by the 15th training cycle, confirming its high stability and performance. Future research could focus on optimizing the proposed approach to handle even more complex and dynamic video sequences and improving data processing to reduce computational costs and increase processing speed. |
| format | Article |
| id | doaj-art-a456f819ee964a7bb3fa37ab14c9df5c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a456f819ee964a7bb3fa37ab14c9df5c2024-11-20T00:01:00ZengIEEEIEEE Access2169-35362024-01-011216923516924610.1109/ACCESS.2024.348778310737329Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-TimeZhazira Mutalova0https://orcid.org/0000-0001-9912-5978Anargul Shaushenova1https://orcid.org/0000-0002-3164-3688Ardak Nurpeisova2https://orcid.org/0000-0002-1245-8313Maral Ongarbayeva3Aidar Ispussinov4https://orcid.org/0009-0001-4134-776XSandugash Bekenova5https://orcid.org/0000-0001-7707-5623Zhanar Altynbekova6S. Seifullin Kazakh Agrotechnical Research University, Astana, KazakhstanS. Seifullin Kazakh Agrotechnical Research University, Astana, KazakhstanS. Seifullin Kazakh Agrotechnical Research University, Astana, KazakhstanSherkhan Murtaza International Taraz Institute, Taraz, KazakhstanS. Seifullin Kazakh Agrotechnical Research University, Astana, KazakhstanWest Kazakhstan Agricultural and Technical University named after Zhangir Khan, Uralsk, KazakhstanSherkhan Murtaza International Taraz Institute, Taraz, KazakhstanThe development of mathematical models for detecting moving objects in real-time video streams is a significant task, especially in light of the growing demands for security and process automation. This paper proposes a detection system based on the Faster Region-Based Convolutional Neural Network (Faster R-CNN) model, utilizing various backbone architectures, including Residual Network-50 (ResNet-50), Feature Pyramid Network (FPN), MobileNet Version 3 Large, and Efficient Network-B0 (EfficientNetB0) with a Self-Attention mechanism. The model extracts object features by combining deep neural networks and scaling mechanisms, enabling adaptation to various operating conditions. Experimental results demonstrate that the proposed approach achieves high accuracy, with the MobileNet Version 3 Large model achieving 95.70% accuracy and the Residual Network-50 model reaching 100% accuracy within the first three training cycles (epochs). The Faster Region-Based Convolutional Neural Network model using Efficient Network-B0 with the Self-Attention mechanism reaches 100% accuracy by the third training cycle, maintaining consistent performance throughout the remaining training cycles. The model with the Residual Network-50 and Feature Pyramid Network backbone architecture shows an average loss reduction from 0.0922 in the first training cycle to 0.0102 by the 15th training cycle, confirming its high stability and performance. Future research could focus on optimizing the proposed approach to handle even more complex and dynamic video sequences and improving data processing to reduce computational costs and increase processing speed.https://ieeexplore.ieee.org/document/10737329/Real-time object detectionvideo stream processingface embeddingsmulti-threaded recognitionmachine learningobject classification |
| spellingShingle | Zhazira Mutalova Anargul Shaushenova Ardak Nurpeisova Maral Ongarbayeva Aidar Ispussinov Sandugash Bekenova Zhanar Altynbekova Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time IEEE Access Real-time object detection video stream processing face embeddings multi-threaded recognition machine learning object classification |
| title | Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time |
| title_full | Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time |
| title_fullStr | Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time |
| title_full_unstemmed | Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time |
| title_short | Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time |
| title_sort | development of a mathematical model for detecting moving objects in video streams in real time |
| topic | Real-time object detection video stream processing face embeddings multi-threaded recognition machine learning object classification |
| url | https://ieeexplore.ieee.org/document/10737329/ |
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