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.
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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.
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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.
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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.
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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.
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Dataset

7.
Sample Dataset
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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
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9.
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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
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Human Detection
(YOLO Model)
Distance
Measurement
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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.
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12.
Single Human Detection
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YOLO Model
Input Image
Human Detected

13.
Multi-Human Detection
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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:
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15.
Penn-Fudan Dataset Output
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Input Frame Output Frame

16.
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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.
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18.
Thank You…
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