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- W2765181503 abstract "Nowadays video surveillance is widely used for public safety. In practice, multiple factors e.g. video compression, would cause the quality degradation and weaken the value of surveillance videos. So, proper quality assessment methods are needed for guiding the deployment and configuration of surveillance video system. In general, surveillance video quality assessment (SVQA) is different from conventional video quality assessment, because surveillance videos are usually used for one specific task e.g. recognition. We propose a face recognition (FR) task driven SVQA framework. In this paper, we mainly focus on one newly defined FR task: distorted face recognition (DFR) task, which is illustrated in Fig.1 (a). Our goal is to establish an objective DFR model which can be used to measure the quality of distorted videos when compared to reference videos. To do that, first, we construct a face dataset collected from the real-world surveillance videos considering multiple factors e.g. light intensity, compression level, and conduct subjective experiments to collect subjective labels for the DFR task. Based on subjective labels we learn an objective distorted face recognition model and take it to assess the quality of distorted surveillance videos. In objective experiments, we analyze how different factors affect the quality of surveillance videos. In addition, the comparisons to PSNR and SSIM are made to show the advantages of the proposed method. At last, we give some suggestions for the practical applications of our proposed SVQA framework." @default.
- W2765181503 created "2017-11-10" @default.
- W2765181503 creator A5076907429 @default.
- W2765181503 creator A5081555196 @default.
- W2765181503 date "2017-10-23" @default.
- W2765181503 modified "2023-09-24" @default.
- W2765181503 title "Surveillance Video Quality Assessment Based on Face Recognition" @default.
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- W2765181503 doi "https://doi.org/10.1145/3126686.3130239" @default.
- W2765181503 hasPublicationYear "2017" @default.
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