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Secure Moving Object Detection in Compressed Video Using Attentions
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Summary
Researchers present an attention-based framework for privacy-preserving Moving Object Detection (MOD) in compressed video, where the video is encrypted on the user side and CVSE data is extracted from the encrypted video in the cloud. A two-stage training process is used: a deep learning approach interpolates CVSE-based motion feature maps, and a specialized backbone with an attention-based neck handles sparse CVSE data. On the VIRAT and DUKE-MTMC datasets, the framework reports state-of-the-art detection performance and greater robustness in challenging scenes than previous secure solutions.