Human re-identification in real-world surveillance camera networks

Abstract

Video surveillance has become critical for security applications. With cameras and data storage devices getting more affordable, many institutions and organizations have chosen to install camera networks for safety and surveillance. The U.S. Department of Homeland Security, for instance, has directed a huge amount of manpower and expenditure to the installation, maintenance, replacement and operation of surveillance camera networks. Public transportation centers such as airports, train stations, and bus stops are some of the most concentrated environments. The traditional monitoring method, which completely relies on security officers' observations, becomes less feasible when more and more screens need to be watched at the same time. Instead, video analytic solutions that process multiple cameras simultaneously are more reliable. In this thesis, we focus on one particular application, human re-identification, with a focus on the challenges of real-world scenarios.