Video Quality Assessment Dataset for Smart Public Security Systems
| Author | Bezzine I. |
| Author | Khan Z.A. |
| Author | Beghdadi A. |
| Author | Al-Maadeed N. |
| Author | Kaaniche M. |
| Author | Al-Maadeed, Somaya |
| Author | Bouridane A. |
| Author | Cheikh F.A. |
| Available date | 2022-05-19T10:23:09Z |
| Publication Date | 2020 |
| Publication Name | Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020 |
| Resource | Scopus |
| Identifier | http://dx.doi.org/10.1109/INMIC50486.2020.9318149 |
| Abstract | Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems. Database link: https://data.mendeley.com/datasets/prf8p9rwhd/draft?a=d9646d2f-5154-4d49-8eb3-e7989187a24c |
| Sponsor | VII. ACKNOWLEDGMENTS This work was made possible by NPRP grant number NPRP8-140-2-065 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
| Language | en |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Subject | Face recognition Monitoring Face detection and recognition Objective video quality Pair comparisons Security and monitoring Video quality assessment Video signal Video surveillance Visual Tracking Security systems |
| Type | Conference Paper |
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