Abstract
In this paper, human detection and social distance analysis are discussed for both congenital and acquired diseases. Mitigating such pandemics can be done by maintaining social distance and avoiding overcrowding. In such conditions, self-taken measures like hand washing, environmental cleaning, facemask use, and avoiding overcrowding, and maintaining social distance are effective. It is suggested that by following these small steps, the spread of the virus could be minimized. This study uses OpenCV for both object detection and object tracking. Additionally, it is used to calculate social distance between people. An object tracking algorithm is used to identify people's presence movement detection through PIR sensor, whereas the human face detection is tracked by camera module. The standard distance for calculating distance is six feet because it is the distance needed to maintain social distance. Models were trained and then used for both object detection and object tracking, with the most popular one being OpenCV. A custom dataset was created for training this model, and it was used to understand and identify the human face. In addition to detecting and tracking people accurately, the developed model can be applied in other areas of research where humans are the primary focus, for instance, self-driving cars, crowd analysis, or any other type of research that prioritizes human detection. A significant challenge is that people behave in unpredictable ways during gatherings, so the proposed system might prove powerful in dealing with people's unpredictable behavior. In the present study, we evaluate if the distance between people in halls is maintained and estimate the number of people gathering in those halls.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kuchhold M, Simon M, Eiselein V, Sikora T Scale-adaptive real-time crowd detection and counting for drone images. https://sci-hub.mksa.top/https://doi.org/10.1109/ICIP.2018.8451289
Zhao P, Adnan KA, Lyu X, Wei S, Sinnott RO Estimating the size of crowds through deep learning. https://sci-hub.hkvisa.net/https://doi.org/10.1109/CSDE50874.2020.9411377
Greenstone M, Nigam V (2020) Does social distancing matter? University of Chicago, Becker Friedman Institute for Economics Working Paper (2020–26)
Hao N, Minglei T, Luyuan F et al (2019) Application of multi-task and multi-level CNN in crowd counting. Comput Eng Appl. https://doi.org/10.3778/j.issn.1002-8331.1808-0278
Santhini C, Gomathi V Crowd scene analysis using deep learning network. https://doi.org/10.1109/ICCTCT.2018.8550851
Rajendran L, Shankaran RS Big-data enabled real-time crowd surveillance using artificial intelligence and deep learning. https://doi.org/10.1109/BigComp51126.2021.00032
Wu H (2021) High-accuracy crowd counting method based on mixed labeled dataset. In: 2021 IEEE 2nd international conference on big data, artificial intelligence and internet of things engineering (ICBAIE 2021)
Meiyun C, Bisheng W, Guo C et al (2020) Crowd counting method based on pixel-level attention mechanism. Comput Appl 40(1):56–61. https://doi.org/10.11772/j.issn.1001-9081.2019050920
Alleviation of COVID by means of social distancing and face mask detection using YOLO V4. In: 2021 international conference on communication information and computing technology (ICCICT). https://doi.org/10.1109/ICCICT50803.2021.9510168
Indulkar Y Alleviation of COVID by means of social distancing and face mask detection using YOLO V4. https://www.analyticsvidhya.com/blog/2021/05/alleviation-of-covid-by-means-of-social-distancing-face-mask-detection-using-yolo-v4/
Rahim A, Maqbool A, Rana T Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera. https://journals.plos.org/plosone/article?id=https://doi.org/10.1371/journal.pone.0247440
Kanjo E, Anderez DO, Anwar A, Alshami A, William J CrowdTracing: overcrowding clustering and detection system for social distancing. https://www.techrxiv.org/articles/preprint/CrowdTracing_Overcrowding_Clustering_and_Detection_System_for_Social_Distancing/14709762
Punn NS, Sonbhadra SK, Agarwal S, Rai G Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. https://arxiv.org/pdf/2005.01385.pdf
Pre-training convolution network for crowd counting. https://doi.org/10.1109/CTISC52352.2021.00062, https://ieeexplore.ieee.org/document/9527619
W. H. Organization (2020) WHO corona-viruses (COVID-19). https://www.who.int/emergencies/diseases/novel-corona-virus-2019. [Online]. Accessed 02 May 2020
Hemangi B, Nikhita K (2016) People counting system using raspberry pi with Opencv. 2(1). ISSN 2494–9150
Subashree D, Mhaske SR, Yeshwantrao SR, Kumar A (2021) Real time crowd counting using OpenCV. 10(05). Paper ID: IJERTV10IS050147
Baul A, Kuang W, Zhang J, Yu H, Wu L Learning to detect pedestrian flow in traffic intersections from synthetic data. https://doi.org/10.1109/ITSC48978.2021.9564853
Sabancı K, Yigit E, Üstün D, Toktaş A, Çelik Y (2018) Thingspeak based monitoring IoT system for counting people in a library. In: 2018 international conference on artificial intelligence and data processing (IDAP). https://doi.org/10.1109/IDAP.2018.8620793
Herviana A, Sudiharto DW, Yulianto FA The prototype of in-store visitor and people passing counters using single shot detector performed by OpenCV. https://doi.org/10.1109/ICITAMEE50454.2020.9398507
Madhira K, Shukla A Pedestrian flow counter using image processing. https://doi.org/10.1109/ICECDS.2017.8389782
B. News (2020) China coronavirus: lockdown measures rise across Hubei province. https://www.bbc.co.uk/news/world-asia-china51217455. [Online]. Accessed 23 January 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Girhepunje, M., Jain, S., Ramteke, T., Wyawahare, N.P., Khobragade, P., Wazalwar, S. (2023). Proposed Crowd Counting System and Social Distance Analyzer for Pandemic Situation. In: Tiwari, R., Pavone, M.F., Ravindranathan Nair, R. (eds) Proceedings of International Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-2126-1_32
Download citation
DOI: https://doi.org/10.1007/978-981-19-2126-1_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2125-4
Online ISBN: 978-981-19-2126-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)