Enhancing real human detection and people counting using YOLOv8
The ability to accurately recognize and count persons is crucial in many real-world applications, including surveillance, security, and crowd management, making it one of computer vision’s most fundamental tasks. You Only Look Once (YOLO) is one of the most effective deep learning models for object...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00061.pdf |
| _version_ | 1797213261758726144 |
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| author | Abdul Ridha Shyaa Tahreer Hashim Ahmed A. |
| author_facet | Abdul Ridha Shyaa Tahreer Hashim Ahmed A. |
| author_sort | Abdul Ridha Shyaa Tahreer |
| collection | DOAJ |
| description | The ability to accurately recognize and count persons is crucial in many real-world applications, including surveillance, security, and crowd management, making it one of computer vision’s most fundamental tasks. You Only Look Once (YOLO) is one of the most effective deep learning models for object identification and counting in recent years. This research seeks to learn more about the YOLOv8 algorithm for precisely counting people in still photos and moving videos. The YOLO method has been at the forefront of computer vision due to its ability to recognize things in real time. People in a crowd typically overlap and block one other, and perspective effects can result in enormous changes in human size, shape, and appearance in the image, all of which make accurate headcounts challenging.The YOLO methodology and its adaptation for population census are the subject of this research. Results from experiments support the usefulness of the proposed approach. Surveillance, crowd control, traffic monitoring, retail analytics, event management, and urban planning are just some of the potential uses highlighted by the findings of this study. Mean Average Precision (MAP) numbers demonstrate that the identification procedure was successful, and the counting process was accurate to within 100%. |
| first_indexed | 2024-04-24T10:55:28Z |
| format | Article |
| id | doaj.art-8622bcbfd18c4b08a8473dffb8db4259 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:55:28Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-8622bcbfd18c4b08a8473dffb8db42592024-04-12T07:36:22ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970006110.1051/bioconf/20249700061bioconf_iscku2024_00061Enhancing real human detection and people counting using YOLOv8Abdul Ridha Shyaa Tahreer0Hashim Ahmed A.1The Iraqi Commission for Computers and Informatics, The Informatics Institute for Postgraduate Studies, The department of Computer Science BaghdadUniversity of Information Technology and Communications, College of Engineering BaghdadThe ability to accurately recognize and count persons is crucial in many real-world applications, including surveillance, security, and crowd management, making it one of computer vision’s most fundamental tasks. You Only Look Once (YOLO) is one of the most effective deep learning models for object identification and counting in recent years. This research seeks to learn more about the YOLOv8 algorithm for precisely counting people in still photos and moving videos. The YOLO method has been at the forefront of computer vision due to its ability to recognize things in real time. People in a crowd typically overlap and block one other, and perspective effects can result in enormous changes in human size, shape, and appearance in the image, all of which make accurate headcounts challenging.The YOLO methodology and its adaptation for population census are the subject of this research. Results from experiments support the usefulness of the proposed approach. Surveillance, crowd control, traffic monitoring, retail analytics, event management, and urban planning are just some of the potential uses highlighted by the findings of this study. Mean Average Precision (MAP) numbers demonstrate that the identification procedure was successful, and the counting process was accurate to within 100%.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00061.pdf |
| spellingShingle | Abdul Ridha Shyaa Tahreer Hashim Ahmed A. Enhancing real human detection and people counting using YOLOv8 BIO Web of Conferences |
| title | Enhancing real human detection and people counting using YOLOv8 |
| title_full | Enhancing real human detection and people counting using YOLOv8 |
| title_fullStr | Enhancing real human detection and people counting using YOLOv8 |
| title_full_unstemmed | Enhancing real human detection and people counting using YOLOv8 |
| title_short | Enhancing real human detection and people counting using YOLOv8 |
| title_sort | enhancing real human detection and people counting using yolov8 |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00061.pdf |
| work_keys_str_mv | AT abdulridhashyaatahreer enhancingrealhumandetectionandpeoplecountingusingyolov8 AT hashimahmeda enhancingrealhumandetectionandpeoplecountingusingyolov8 |