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Social Distancing Detection and Crowd
Monitoring System: A Computer Vision
Approaches during COVID-19
Objective
• To estimate social distance violations between people and
crowed monitoring system using Computer Vision methods.
27 January 2023 2
Research Scholar name:
Introduction
• The ongoing COVID-19 corona virus outbreak has caused a global
disaster with its deadly spreading.
• Due to the absence of effective remedial agents and the shortage of
immunizations against the virus, population vulnerability increases.
• In the current situation, social distancing is thought to be an adequate
precaution (norm) against the spread of the pandemic virus.
• In the past decade, AI/Deep Learning has shown promising results in
several daily life problems. Various daily life tasks have been automated
with the help of AI.
27 January 2023 3
Research Scholar name:
• Social-distancing is an important way to slow down the spread
infectious diseases. People are asked to limit their interactions w
each other, reducing the chances of the disease being spread w
physical or close contact.
• In the past decade, AI/Deep Learning has shown promising results
several daily life problems. Various daily life tasks have be
automated with the help of AI.
27 January 2023 4
Research Scholar name:
Introduction
• The only way to prevent the spread of COVID-19 is Social Distancing.
Keeping a safe distance from each other is the ultimate way to
prevent spread in the community.
• So this got me thinking – I want to build a tool that can potentially
detect where each person is in real-time, and return a bounding box
that turns red if the distance between two people is dangerously
close.
• This can be used by governments to analyze the movement of people
and alert them if the situation turns serious.
27 January 2023 5
Research Scholar name:
Motivation of this work
• Name of the Database - Penn-Fudan Database
• This is an image database containing images that are used for pedestrian
detection in the experiments. The images are taken from scenes around
campus and urban street. The objects we are interested in these images
are pedestrians. Each image will have at least one pedestrian in it.
• The heights of labeled pedestrians in this database fall into [180,390]
pixels. All labeled pedestrians are straight up.
• There are 170 images with 345 labeled pedestrians, among which 96
images are taken from around University of Pennsylvania, and other 74
are taken from around Fudan University.
27 January 2023 6
Research Scholar name:
Dataset
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6. Work6 Social Distancing.pptx

  • 1. Social Distancing Detection and Crowd Monitoring System: A Computer Vision Approaches during COVID-19
  • 2. Objective • To estimate social distance violations between people and crowed monitoring system using Computer Vision methods. 27 January 2023 2 Research Scholar name:
  • 3. Introduction • The ongoing COVID-19 corona virus outbreak has caused a global disaster with its deadly spreading. • Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. • In the current situation, social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. • In the past decade, AI/Deep Learning has shown promising results in several daily life problems. Various daily life tasks have been automated with the help of AI. 27 January 2023 3 Research Scholar name:
  • 4. • Social-distancing is an important way to slow down the spread infectious diseases. People are asked to limit their interactions w each other, reducing the chances of the disease being spread w physical or close contact. • In the past decade, AI/Deep Learning has shown promising results several daily life problems. Various daily life tasks have be automated with the help of AI. 27 January 2023 4 Research Scholar name: Introduction
  • 5. • The only way to prevent the spread of COVID-19 is Social Distancing. Keeping a safe distance from each other is the ultimate way to prevent spread in the community. • So this got me thinking – I want to build a tool that can potentially detect where each person is in real-time, and return a bounding box that turns red if the distance between two people is dangerously close. • This can be used by governments to analyze the movement of people and alert them if the situation turns serious. 27 January 2023 5 Research Scholar name: Motivation of this work
  • 6. • Name of the Database - Penn-Fudan Database • This is an image database containing images that are used for pedestrian detection in the experiments. The images are taken from scenes around campus and urban street. The objects we are interested in these images are pedestrians. Each image will have at least one pedestrian in it. • The heights of labeled pedestrians in this database fall into [180,390] pixels. All labeled pedestrians are straight up. • There are 170 images with 345 labeled pedestrians, among which 96 images are taken from around University of Pennsylvania, and other 74 are taken from around Fudan University. 27 January 2023 6 Research Scholar name: Dataset
  • 7. Sample Dataset 27 January 2023 7 Research Scholar name:
  • 8. Literature Survey S. No. Year Author Contribution 1 2020 Yew Cheong Hou, et.al Social Distancing Detection with Deep Learning Model 2 2021 D. Mohanapriya, et.al Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19 27 January 2023 8 Research Scholar name: Research Scholar name:
  • 9. 27 January 2023 9 Research Scholar name: S. No. Year Author Contribution 3 2020 Degadwala, et.al Visual Social Distance Alert System Using Computer Vision & Deep Learning 4 2021 G V Shalini, et.al Social Distancing Analyzer Using Computer Vision and Deep Learning Literature Survey
  • 10. Overall Architecture 27 January 2023 10 Human Detection (YOLO Model) Distance Measurement Research Scholar name: Input Video Frame Display Social Distance Violations Input Video Frame Human Detection (YOLO Model) Display Social Distance Violations
  • 11. Human Detection • In this work, the YOLO model was adopted for pedestrian detection. • The YOLO algorithm was considered as an object detection taking a given input image and simultaneously learning bounding box coordinates (tx, ty, tw, th), object confidence and corresponding class label probabilities (P1, P2, …, Pc). • The YOLO trained on the COCO dataset which consists of 80 labels including human or pedestrian class. 27 January 2023 11 Research Scholar name:
  • 12. Single Human Detection 27 January 2023 12 Research Scholar name: YOLO Model Input Image Human Detected
  • 13. Multi-Human Detection 27 January 2023 13 Research Scholar name: YOLO Model Input Image Human Detected
  • 14. Distance measurement • In this step of the pipeline, the location of the bounding box for each person (x, y, w, h) in the perspective view is detected and transformed into a top-down view. • For each pedestrian, the position in the top-down view is estimated based on the bottom-center point of the bounding box. • Given the position of two pedestrians in an image as (x1, y1) and (x2, y2) respectively, the distance between the two pedestrians, d, can be computed as: 27 January 2023 14 Research Scholar name:
  • 15. Penn-Fudan Dataset Output 27 January 2023 15 Research Scholar name: Input Frame Output Frame
  • 16. 27 January 2023 Research Scholar name: 16 Penn-Fudan Dataset Output Input Frame Output Frame
  • 17. References 1. Landing AI Creates an AI Tool to Help Customers Monitor Social Distancing in the Workplace [Onlive]. Available at https://landing.ai/landing-ai-creates-an-ai-tool-to-help-customersmonitor- social-distancing-in-the-workplace/ (Access on 4 May 2020) 2. J. Redmon, A. Farhadi, “Yolov3: An incremental improvement”, arXiv preprint arXiv:1804.02767, 2018. 3. K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016. 4. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, “Rethinking the inception architecture for computer vision”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818- 2826, 2016. 5. D.T. Nguyen, W. Li, P.O. Ogunbona, “Human detection from images and videos: A survey”, Pattern Recognition, 51:148-75, 2016. 6. A. Krizhevsky, I. Sutskever, G.E. Hinton, “Imagenet classification with deep convolutional neural networks”, In Advances in neural information processing systems, pp. 1097-1105, 2012. 27 January 2023 17 Research Scholar name:
  • 18. Thank You… 27 January 2023 Research Scholar name: 18