Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities
Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd den...
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/11187 |
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| author | Mesfer Al Duhayyim Eatedal Alabdulkreem Khaled Tarmissi Mohammed Aljebreen Bothaina Samih Ismail Abou El Khier Abu Sarwar Zamani Ishfaq Yaseen Mohamed I. Eldesouki |
| author_facet | Mesfer Al Duhayyim Eatedal Alabdulkreem Khaled Tarmissi Mohammed Aljebreen Bothaina Samih Ismail Abou El Khier Abu Sarwar Zamani Ishfaq Yaseen Mohamed I. Eldesouki |
| author_sort | Mesfer Al Duhayyim |
| collection | DOAJ |
| description | Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods. |
| first_indexed | 2024-03-09T19:17:34Z |
| format | Article |
| id | doaj.art-dde3fb3886f446b2b67a7baeb45b5f3b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:34Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dde3fb3886f446b2b67a7baeb45b5f3b2023-11-24T03:39:27ZengMDPI AGApplied Sciences2076-34172022-11-0112211118710.3390/app122111187Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart CitiesMesfer Al Duhayyim0Eatedal Alabdulkreem1Khaled Tarmissi2Mohammed Aljebreen3Bothaina Samih Ismail Abou El Khier4Abu Sarwar Zamani5Ishfaq Yaseen6Mohamed I. Eldesouki7Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi ArabiaDepartment of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi ArabiaDepartment of Architectural Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11845, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Information System, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaVideo surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods.https://www.mdpi.com/2076-3417/12/21/11187sustainabilitysmart citiesdeep learningcrowd densityvideo surveillance |
| spellingShingle | Mesfer Al Duhayyim Eatedal Alabdulkreem Khaled Tarmissi Mohammed Aljebreen Bothaina Samih Ismail Abou El Khier Abu Sarwar Zamani Ishfaq Yaseen Mohamed I. Eldesouki Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities Applied Sciences sustainability smart cities deep learning crowd density video surveillance |
| title | Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities |
| title_full | Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities |
| title_fullStr | Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities |
| title_full_unstemmed | Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities |
| title_short | Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities |
| title_sort | aquila optimization with transfer learning based crowd density analysis for sustainable smart cities |
| topic | sustainability smart cities deep learning crowd density video surveillance |
| url | https://www.mdpi.com/2076-3417/12/21/11187 |
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