Real-World Re-Identification in an Airport Camera Network

Human re-identification across non-overlapping fields of view is one of the fundamental problems in video surveillance. While most reported research for this problem is focused on improving the matching rate between pairs of cropped rectangles around humans, the situation is quite different when it comes to creating a re-identification algorithm that operates robustly in the real world. In this paper, we describe an end-to-end system solution of the re-identification problem installed in an airport environment, with a focus on the challenges brought by the real world scenario. We discuss the high-level system design of the video surveillance application, and the issues we encountered during our development and testing. We also describe the algorithm framework for our human re-identification software, and discuss considerations of speed and matching performance. Finally, we report the results of an experiment conducted to illustrate the output of the developed software as well as its feasibility for the airport surveillance task.