Public Social Distance Monitoring System Using Object Detection YOLO Deep Learning Algorithm

Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best...

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Vydáno v:SN computer science Ročník 4; číslo 6; s. 718
Hlavní autoři: R, Vijayan, V, Mareeswari, Pople, Vedant
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.11.2023
Springer Nature B.V
Témata:
Accuracy
Algorithms
Applications programs
Classification
Comparative analysis
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Computer vision
Coronaviruses
COVID-19
Data Structures and Information Theory
Deep learning
Disease prevention
Disease transmission
Energy consumption
Frames (data processing)
Frames per second
Graphics processing units
Information Systems and Communication Service
Machine learning
Mobile computing
Monitoring
Monitoring systems
Neural networks
New technology
Object recognition
Original Research
Pandemics
Pattern Recognition and Graphics
Research Trends in Computational Intelligence
Security systems
Social distancing
Software Engineering/Programming and Operating Systems
Video data
Violations
Vision
YOLO
Deep learning
Computer vision
Object classification
Social monitoring
Object detection
ISSN:2661-8907, 2662-995X, 2661-8907
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Popis
Shrnutí:Computer vision and deep learning are emerging technologies as the backbone system to maintain the public healthcare sector to detect the object and surrounding, especially during the COVID-19 pandemic. Generally, in a single stage, you only look once version 3 (YOLOv3) algorithms promising the best results to detect the object in images, live feeds, or videos by learning features at a faster rate than two-stage algorithms such as R-CNN, fast CNN, and faster CNN. Deep sort methods were employed to track identified people by supporting bounding boxes and calculating the Euclidian distances between the people to maintain social distance. Moreover, the YOLOV3 model requires more computational cost to detect the object at best with a lower detection time. Hence, it motivates us to practice a single graphics processing unit (GPU) with the multithreaded approach to increase the frames per second at detection. The proposed model uses a background modeling method grounded on frame variance accumulation which is used to define the number of frames and weight updating. This approach uses two steps, localization of the object and then the classification of localized objects. Distances between people are calculated and compared with threshold values to facilitate comparison. The threshold limit triggers the alert system which is accessible to people, monitoring many video streams at a time. The model is tested based on processors, threads consumed, and various types of inputs ranging from static images to moving videos. Tiny-YOLOv3 performs with the best frames per second and the least processing time, followed by SPP-YOLOv3 and YOLOv3. The model proves its evidence on various parameters and metrics to work robustly. As well as the reason to adopt YOLOv3 over other YOLOv4 and YOLOV5 is tabulated. This model initiates the curiosity to develop a mobile application with security systems based on IoT and CCTV to monitor crowded places. Graphical Abstract
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-02131-2