| Autor: |
Kang Hao Cheong, Sandra Poeschmann, Joel Weijia Lai, Jin Ming Koh, U. Rajendra Acharya, Simon Ching Man Yu, Kenneth Jian Wei Tang |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 7, Pp 183252-183261 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2958255 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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