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Sunshine-Change-Tolerant Moving Object Masking for Realizing both Privacy Protection and Video Surveillance
Yoichi TOMIOKA Hikaru MURAKAMI Hitoshi KITAZAWA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2014/09/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
privacy protection, video surveillance, background subtraction, Real AdaBoost, sunshine change,
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Recently, video surveillance systems have been widely introduced in various places, and protecting the privacy of objects in the scene has been as important as ensuring security. Masking each moving object with a background subtraction method is an effective technique to protect its privacy. However, the background subtraction method is heavily affected by sunshine change, and a redundant masking by over-extraction is inevitable. Such superfluous masking disturbs the quality of video surveillance. In this paper, we propose a moving object masking method combining background subtraction and machine learning based on Real AdaBoost. This method can reduce the superfluous masking while maintaining the reliability of privacy protection. In the experiments, we demonstrate that the proposed method achieves about 78-94% accuracy for classifying superfluous masking regions and moving objects.