Watch me from distance (wmd): a privacy-preserving long-distance video surveillance system

Abstract

Preserving the privacy of people in video surveillance systems is quite challenging, and a significant amount of research has been done to solve this problem in recent times. Majority of existing techniques are based on detecting bodily cues such as face and/or silhouette and obscuring them so that people in the videos cannot be identified. We observe that merely hiding bodily cues is not enough for protecting identities of the individuals in the videos. An adversary, who has prior contextual knowledge about the surveilled area, can identify people in the video by exploiting the implicit inference channels such as behavior, place, and time. This article presents an anonymous surveillance system, called Watch Me from Distance (WMD), which advocates for outsourcing of surveillance video monitoring (similar to call centers) to the long-distance sites where professional security operators watch the video and alert the local site when any suspicious or abnormal event takes place. We find that long-distance monitoring helps in decoupling the contextual knowledge of security operators. Since security operators at the remote site could turn into adversaries, a trust computation model to determine the credibility of the operators is presented as an integral part of the proposed system. The feasibility study and experiments suggest that the proposed system provides more robust measures of privacy yet maintains surveillance effectiveness.