Toward Efficient and Intelligent Video Analytics With Visual Privacy Protection for Large-Scale Surveillance

Abstract

Nowadays, the explosion of CCTV cameras has resulted in an increasing demand for distributed solutions to efficiently process the vast volume of video data. Otherwise, the use of surveillance when people are being watched remotely and recorded continuously has raised a significant threat to visual privacy. Using existing systems cannot prevent any party from exploiting unwanted personal data of others. In this paper, we develop an intelligent surveillance system with integrated privacy protection, where it is built on the top of big data tools, i.e., Kafka and Spark Streaming. To protect individual privacy, we propose a privacy-preserving solution based on effective face recognition and tracking mechanisms. Particularly, we associate body pose with face to reduce privacy leaks across video frames. The body pose is also exploited to infer person-centric information like human activities. Extensive experiments conducted on benchmark datasets further demonstrate the efficiency of our system for various vision tasks.