Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems
Crowd Density Detection in Smart Video Surveillance involves advanced computer vision (CV) techniques to improve the efficiency and accuracy of crowd monitoring. The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) mo...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10843209/ |
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| author | Sultan Refa Alotaibi Hanan Abdullah Mengash Mohammed Maray Faiz Abdullah Alotaibi Abdulwhab Alkharashi Ahmad A. Alzahrani Moneerah Alotaibi Mrim M. Alnfiai |
| author_facet | Sultan Refa Alotaibi Hanan Abdullah Mengash Mohammed Maray Faiz Abdullah Alotaibi Abdulwhab Alkharashi Ahmad A. Alzahrani Moneerah Alotaibi Mrim M. Alnfiai |
| author_sort | Sultan Refa Alotaibi |
| collection | DOAJ |
| description | Crowd Density Detection in Smart Video Surveillance involves advanced computer vision (CV) techniques to improve the efficiency and accuracy of crowd monitoring. The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) models on surveillance videos. It detects crowded areas, manages crowd flow, and combines automated analysis with human oversight for improved public safety and early intervention. Explainable Artificial Intelligence (XAI) improves the interpretability and transparency of crowd management methods. Incorporating XAI models provides clear, understandable insights into predictions, ensuring more actionable and reliable crowd management. This study proposes an Osprey Optimization Algorithm with Deep Learning Assisted Crowd Density Detection and Classification (OOADL-CDDC) technique for smart video surveillance systems. The aim of the OOADL-CDDC technique is to enable the automated and efficient detection of distinct kinds of crowd densities. To achieve this, the OOADL-CDDC technique primarily utilizes a bilateral filtering (BF) approach for noise removal process. The OOADL-CDDC technique utilizes an advanced DL method, employing the SE-DenseNet model for feature extraction, while the hyperparameter selection is performed by using the OOA model. Finally, the detection and classification of the crowd density is accomplished by using the attention bidirectional gated recurrent unit (ABiGRU) model. A series of experiments are performed to demonstrate the improved performance of the OOADL-CDDC method. The performance validation of the OOADL-CDDC technique portrayed a superior accuracy value of 98.30% over existing models in terms of distinct measures. |
| format | Article |
| id | doaj-art-e6dc2b7b82784b9ca829369bfc0afaaa |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e6dc2b7b82784b9ca829369bfc0afaaa2025-01-31T23:05:24ZengIEEEIEEE Access2169-35362025-01-0113207502076210.1109/ACCESS.2025.352984310843209Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance SystemsSultan Refa Alotaibi0Hanan Abdullah Mengash1https://orcid.org/0000-0002-4103-2434Mohammed Maray2https://orcid.org/0000-0002-7066-2945Faiz Abdullah Alotaibi3https://orcid.org/0009-0007-1908-4928Abdulwhab Alkharashi4https://orcid.org/0009-0008-6618-2659Ahmad A. Alzahrani5https://orcid.org/0000-0003-1573-0367Moneerah Alotaibi6https://orcid.org/0000-0002-0074-8153Mrim M. Alnfiai7https://orcid.org/0000-0003-3837-6313Department of Computer Science, College of Science and Humanities-Dawadmi, Shaqra University, Shaqra, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science, College of Science and Humanities-Dawadmi, Shaqra University, Shaqra, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaCrowd Density Detection in Smart Video Surveillance involves advanced computer vision (CV) techniques to improve the efficiency and accuracy of crowd monitoring. The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) models on surveillance videos. It detects crowded areas, manages crowd flow, and combines automated analysis with human oversight for improved public safety and early intervention. Explainable Artificial Intelligence (XAI) improves the interpretability and transparency of crowd management methods. Incorporating XAI models provides clear, understandable insights into predictions, ensuring more actionable and reliable crowd management. This study proposes an Osprey Optimization Algorithm with Deep Learning Assisted Crowd Density Detection and Classification (OOADL-CDDC) technique for smart video surveillance systems. The aim of the OOADL-CDDC technique is to enable the automated and efficient detection of distinct kinds of crowd densities. To achieve this, the OOADL-CDDC technique primarily utilizes a bilateral filtering (BF) approach for noise removal process. The OOADL-CDDC technique utilizes an advanced DL method, employing the SE-DenseNet model for feature extraction, while the hyperparameter selection is performed by using the OOA model. Finally, the detection and classification of the crowd density is accomplished by using the attention bidirectional gated recurrent unit (ABiGRU) model. A series of experiments are performed to demonstrate the improved performance of the OOADL-CDDC method. The performance validation of the OOADL-CDDC technique portrayed a superior accuracy value of 98.30% over existing models in terms of distinct measures.https://ieeexplore.ieee.org/document/10843209/Crowd densityvideo surveillancedeep learningosprey optimization algorithmbilateral filtering |
| spellingShingle | Sultan Refa Alotaibi Hanan Abdullah Mengash Mohammed Maray Faiz Abdullah Alotaibi Abdulwhab Alkharashi Ahmad A. Alzahrani Moneerah Alotaibi Mrim M. Alnfiai Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems IEEE Access Crowd density video surveillance deep learning osprey optimization algorithm bilateral filtering |
| title | Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems |
| title_full | Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems |
| title_fullStr | Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems |
| title_full_unstemmed | Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems |
| title_short | Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems |
| title_sort | integrating explainable artificial intelligence with advanced deep learning model for crowd density estimation in real world surveillance systems |
| topic | Crowd density video surveillance deep learning osprey optimization algorithm bilateral filtering |
| url | https://ieeexplore.ieee.org/document/10843209/ |
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