Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/82173
| 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 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Cheong_Automated_Video_Crowd.pdf | 8.91 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
54
Last Week
2
2
Last month
Citations as of Sep 17, 2023
Downloads
85
Citations as of Sep 17, 2023
SCOPUSTM
Citations
28
Citations as of Sep 21, 2023
WEB OF SCIENCETM
Citations
22
Citations as of Sep 21, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.