A privacy-preserving human tracking scheme in centralized cloud based camera networks
Yu‐Chi Chen, Chun-Te Chu, Jenq–Neng Hwang, Jang‐Hee Yoo · 2014
Camera networks have been deployed to facilitate human tracking across multi-cameras in the modern surveillance systems. However, human privacy is an important concern on security surveillance. More specifically, in the real world, surveillance cameras are commonly installed by different entities (such as departments or companies), and any recorded video by one entity should not be shared with others to protect the privacy of tracked humans, while maintaining the knowledge of those moving trajectories of tracked humans in a centralized cloud server. This tracking across multi-cameras information can serve as a very powerful analysis tool for locating crime suspects or collecting business statistics. This paper is the first to aim at the importance of privacy-preserving in a multiple-camera tracking system. We address the problems of privacy-preserving human tracking based on Paillier encryption without revealing any recorded video or data, and introduce the secure multiple-camera system which consists of two stages: training and testing stages. Finally, the security analyses and simulations show the effectiveness of the proposed scheme.