Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images
Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tool...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/12/3092 |
| _version_ | 1797592791377772544 |
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| author | Manal Abdullah Alohali Mohammed Aljebreen Nadhem Nemri Randa Allafi Mesfer Al Duhayyim Mohamed Ibrahim Alsaid Amani A. Alneil Azza Elneil Osman |
| author_facet | Manal Abdullah Alohali Mohammed Aljebreen Nadhem Nemri Randa Allafi Mesfer Al Duhayyim Mohamed Ibrahim Alsaid Amani A. Alneil Azza Elneil Osman |
| author_sort | Manal Abdullah Alohali |
| collection | DOAJ |
| description | Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment. |
| first_indexed | 2024-03-11T01:59:09Z |
| format | Article |
| id | doaj.art-a441b075d42b41429603cfb0dd254f62 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:59:09Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a441b075d42b41429603cfb0dd254f622023-11-18T12:26:20ZengMDPI AGRemote Sensing2072-42922023-06-011512309210.3390/rs15123092Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing ImagesManal Abdullah Alohali0Mohammed Aljebreen1Nadhem Nemri2Randa Allafi3Mesfer Al Duhayyim4Mohamed Ibrahim Alsaid5Amani A. Alneil6Azza Elneil Osman7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi ArabiaDepartment of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar 91431, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaAnomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.https://www.mdpi.com/2072-4292/15/12/3092anomaly detectionintelligent transportation systemsdeep learningremote sensing imagesfederated learning |
| spellingShingle | Manal Abdullah Alohali Mohammed Aljebreen Nadhem Nemri Randa Allafi Mesfer Al Duhayyim Mohamed Ibrahim Alsaid Amani A. Alneil Azza Elneil Osman Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images Remote Sensing anomaly detection intelligent transportation systems deep learning remote sensing images federated learning |
| title | Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images |
| title_full | Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images |
| title_fullStr | Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images |
| title_full_unstemmed | Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images |
| title_short | Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images |
| title_sort | anomaly detection in pedestrian walkways for intelligent transportation system using federated learning and harris hawks optimizer on remote sensing images |
| topic | anomaly detection intelligent transportation systems deep learning remote sensing images federated learning |
| url | https://www.mdpi.com/2072-4292/15/12/3092 |
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