Occupancy Monitoring to Prevent Spread of COVID-19 in Public Places Using AI
- Title
- Occupancy Monitoring to Prevent Spread of COVID-19 in Public Places Using AI
- Creator
- Pardal H.; Kapse M.; Sharma V.; Elangovan N.
- Description
- The chapter aims to automate the counting of people for occupancy monitoring and send an alert email if the occupancy exceeds the defined threshold in case of restricted occupancy guidelines. The study aims to reduce the manual error, effort, and time for people counting and provide a tool for footfall analysis. We propose and implement an occupancy monitoring system by counting the number of people entering and exiting a building/room using cameras and machine learning (ML) algorithms. The Single Shot Detector (SSD) algorithm, which is based on the MobileNet architecture, is used. This project provides an effective process for execution using either a recorded video file or a live stream from a camera. As the system automates counting people, it reduces human effort and error. It provides accurate results on time. The project can be implemented anywhere using a laptop and a camera for capturing the video. Thus, it provides high portability of the project. The system can leverage pre-installed CCTV cameras and systems in colleges, malls, offices, etc. Thus, it requires less additional expenses and is economically friendly for the organization/decision-making authority. This chapter includes implications for various use cases such as ensuring adherence to COVID-19 guidelines by organizations, streamlining janitorial services, prevention of stampedes, improving indoor air quality, improving electricity efficiency, etc. This project fulfills an identified need to automate the people counting process and generate alerts accordingly. 2025 by Apple Academic Press, Inc.
- Source
- Sustainability in Marketing Practice: Strategies for Industry 4.0, pp. 281-294.
- Date
- 2024-01-01
- Publisher
- Apple Academic Press
- Coverage
- Pardal H., Oracle Corporation, Karnataka, Bangalore, India; Kapse M., Symbiosis Center for Management and Human Resource Development, Symbiosis International University, Maharashtra, Pune, India; Sharma V., Symbiosis Center for Management and Human Resource Development, Symbiosis International University, Maharashtra, Pune, India; Elangovan N., School of Business and Management, Christ University, Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100382892-1; 978-177491588-2
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Pardal H.; Kapse M.; Sharma V.; Elangovan N., “Occupancy Monitoring to Prevent Spread of COVID-19 in Public Places Using AI,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18039.