Detection and count of human bodies in a crowd scene based on enhancement features by using the YOLO v5 algorithm
Joint Authors
Ali, Mohammed Abduljabbar
Hussain, Abir Jaafar
Sadiq, Ahmad T.
Source
Iraqi Journal of Computer, Communications and Control Engineering
Issue
Vol. 22, Issue 2 (30 Jun. 2022), pp.125-134, 10 p.
Publisher
Publication Date
2022-06-30
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Crowd detection has various applications nowadays.
However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible.
Detectors in the overlapping zone may therefore overreact.
The proposal uses the YOLO v5 (You Only Look Once) method to improve crowd recognition and counting.
This algorithm is entirely accurate and detects things in real-time.
The idea relies on edge enhancement and pre-processing to solve overlapping feature regions in the image and improve performance.
The CrowdHuman data set is used to train YOLO v5.
The system counts the number of humans in the image to detect a crowd.
Before training, this model enhanced the image with several filters.
The YOLO v5 algorithm distinguishes a person inside a crowd by utilizing the surrounding box on the head and overall body.
Therefore, the number of head detection is x coordinated compared to the body.
Assume the detected heads outnumber the bodies.
A square of the head will be extracted, but not a body square.
Also, cropping the image reduces interference between human beings and enhances the edge features.
Thus, YOLOv5 can detect it.
The idea improves head and body detection by 2.17 and 4.1 percent, respectively.
American Psychological Association (APA)
Ali, Mohammed Abduljabbar& Hussain, Abir Jaafar& Sadiq, Ahmad T.. 2022. Detection and count of human bodies in a crowd scene based on enhancement features by using the YOLO v5 algorithm. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 22, no. 2, pp.125-134.
https://search.emarefa.net/detail/BIM-1492892
Modern Language Association (MLA)
Ali, Mohammed Abduljabbar…[et al.]. Detection and count of human bodies in a crowd scene based on enhancement features by using the YOLO v5 algorithm. Iraqi Journal of Computer, Communications and Control Engineering Vol. 22, no. 2 (Jun. 2022), pp.125-134.
https://search.emarefa.net/detail/BIM-1492892
American Medical Association (AMA)
Ali, Mohammed Abduljabbar& Hussain, Abir Jaafar& Sadiq, Ahmad T.. Detection and count of human bodies in a crowd scene based on enhancement features by using the YOLO v5 algorithm. Iraqi Journal of Computer, Communications and Control Engineering. 2022. Vol. 22, no. 2, pp.125-134.
https://search.emarefa.net/detail/BIM-1492892
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 133-134
Record ID
BIM-1492892