Secure Moving Object Detection in Compressed Video Using Attentions

Moving Object Detection (MOD) can be outsourced to the cloud for computational convenience, in which case the video must be encrypted to protect privacy. Secure video MOD methods designed to perform MOD on encrypted video are still in their infancy. In this paper, we present an attention-based framework for privacy-preserving MOD in compressed videos. On the user side, we adopt selective video encryption for the compressed video, while in the cloud, we extract the Compressed Video entropy-coded Syntax Elements (CVSE) from the encrypted video. Since the extracted CVSE data lacks sufficient motion information and contains noise, we introduce a two-stage training process. In the first phase, we propose a new deep learning-based approach for interpolating CVSE-based motion feature maps, addressing a significant drawback of traditional methods that rely exclusively on empirical interpolation algorithms. In the second stage, we propose a specialized backbone tailored for feature extraction from sparse CVSE data. We then design an attention-based neck that focuses on areas with denser motion and varying sizes of moving objects. Experimental results on two public datasets, VIRAT and DUKE-MTMC, show that our framework achieves state-of-the-art detection performance. Compared to previous secure solutions, the proposed method exhibits more robustness in challenging scenes.