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However, the transmission of data from cameras to a backend raises substantial privacy concerns, particularly regarding sensitive information like facial data. To offer privacy protection, visual processing techniques, such as Generative Adversarial Networks (GANs), have been employed on cameras to blur and safeguard such data intelligently. However, these techniques frequently face memory challenges, particularly when dealing with high-resolution videos. In this paper, we propose PIMO, a memory-efficient visual privacy protection scheme designed to effectively blur video content leveraging adaptive slicing of frames and resolution degradation. Our extensive experimental evaluations validate that PIMO\u2019s adaptive mechanism proficiently navigates fluctuating memory constraints. 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