In:
ACM Transactions on Multimedia Computing, Communications, and Applications, Association for Computing Machinery (ACM), Vol. 18, No. 1 ( 2022-01-31), p. 1-27
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.
Type of Medium:
Online Resource
ISSN:
1551-6857
,
1551-6865
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2022
detail.hit.zdb_id:
2182650-X
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