Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82173
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Title: Practical automated video analytics for crowd monitoring and counting
Authors: Cheong, KH
Poeschmann, S
Lai, JW
Koh, JM
Acharya, UR
Yu, SCM 
Tang, KJW
Issue Date: 2019
Source: IEEE access, 6 Dec. 2019, v. 7, p. 183252-183261
Abstract: Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closedcircuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of readily-deployable automated surveillance systems. In this study, we present a low-cost and efficient approach that integrates the use of computational object recognition to perform fully-automated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.
Keywords: Crowd monitoring
Counting
Traffic monitoring
Data analytics
Background subtraction
Security
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2958255
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication K. H. Cheong et al., "Practical Automated Video Analytics for Crowd Monitoring and Counting," in IEEE Access, vol. 7, pp. 183252-183261, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2958255
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