Investigating open-world person re-identification using a drone

Abstract

Person re-identification is now one of the most topical and intensively studied problems in computer vision due to its challenging nature and its critical role in underpinning many multi-camera surveillance tasks. A fundamental assumption in almost all existing reidentification research is that cameras are in fixed emplacements, allowing the explicit modelling of camera and inter-camera properties in order to improve re-identification. In this paper, we present an introductory study pushing re-identification in a different direction: re-identification on a mobile platform, such as a drone. We formalise some variants of the standard formulation for re-identification that are more relevant for mobile re-identification. We introduce the first dataset for mobile reidentification, and we use this to elucidate the unique challenges of mobile re-identification. Finally, we re-evaluate some conventional wisdom about re-id models in the light of these challenges and suggest future avenues for research in this area.