Towards Privacy-Aware Smart Surveillance

Abstract

Cameras are rapidly becoming a daily part of our lives. Their constant streaming of information about people gives rise to different security and privacy concerns. Human analysis using cameras or surveillance footage has been an active field of research. Different methods have been introduced which showed success in both the detection and tracking of pedestrians. Once a human is detected and/or tracked, different motion analyses can be performed in order to better understand and model human behavior. A majority of these methods do not take user privacy or security into account, making security monitoring systems a significant threat to individuals’ privacy. This threat becomes more serious and evident when the security cameras are installed in places where vulnerable people (e.g. elders, children) frequently spend time such as day-cares, schools, retirement homes, or violated to serve independent interests. This work presents a model that is able to understand human motion, and deploys an anonymization technique that facilitates the preservation of an individual’s privacy and security.