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Privacy-preserving Motion Detection for HEVC-compressed Surveillance Video

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Published:27 January 2022Publication History
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Abstract

In the cloud era, a large amount of data is uploaded to and processed by public clouds. The risk of privacy leakage has become a major concern for cloud users. Cloud-based video surveillance requires motion detection, which may reveal the privacy of people in a surveillance video. Privacy-preserving video surveillance allows motion detection while protecting privacy. The existing scheme [25], designed to detect motion on encrypted and H.264-compressed surveillance videos, does not work well on more advanced video compression schemes such as HEVC.

In this article, we propose the first motion detection method on encrypted and HEVC-compressed videos. It adopts a novel approach that exploits inter-prediction reference relationships among coding blocks to detect motion regions. The partition pattern and the number of coding bits of each detection block used in prior art are also used to help detect motion regions. Spatial and temporal consistency of a moving object and Kalman filtering are applied to segment connected/merged motion regions, remove noise and background motions, and refine trajectories and shapes of detected moving objects. Experimental results indicate that our detection method achieves high detection recall, precision, and F1-score for surveillance videos of both high and low resolutions with various scenes. It has a similarly high detection accuracy on encrypted and HEVC-compressed videos as that of the existing motion detection method [25] on encrypted and H.264-compressed videos. Our proposed method incurs no bit-rate overhead and has a very low computational complexity for both motion detection and encryption of HEVC videos.

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        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 1
          January 2022
          517 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3505205
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Publication History

          • Published: 27 January 2022
          • Accepted: 1 June 2021
          • Revised: 1 May 2021
          • Received: 1 October 2020
          Published in tomm Volume 18, Issue 1

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