Practical Automated Video Analytics for Crowd Monitoring and Counting

Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closed-circuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of re...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.183252-183261
Main Authors: Cheong, Kang Hao, Poeschmann, Sandra, Lai, Joel Weijia, Koh, Jin Ming, Acharya, U. Rajendra, Yu, Simon Ching Man, Tang, Kenneth Jian Wei
Format: Article
Language:English
Subjects:
Automation
background subtraction
Closed circuit television
counting
Crowd monitoring
data analytics
Facilities management
Lighting
Object recognition
Public spaces
Security
Software
Surveillance
Surveillance systems
Task analysis
Tracking
Traffic management
traffic monitoring
Video data
Video surveillance
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Summary:Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closed-circuit 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2958255