Geometry-Based Person Re-Identification in Fisheye Stereo

Person re-identification using rectilinear cameras has been thoroughly researched to date. However, the topic has received little attention for fisheye cameras and the few developed methods are appearance-based. We propose a geometry-based approach to re-identification for overhead fisheye cameras with overlapping fields of view. The main idea is that a person visible in two camera views is uniquely located in the view of one camera given their height and location in the other camera’s view. We develop a height-dependent mathematical relationship between these locations using the unified spherical model for omnidirectional cameras. We also propose a new fisheye-camera calibration method and a novel automated approach to calibration-data collection. Finally, we propose four re-identification algorithms that leverage geometric constraints and demonstrate their excellent accuracy, which vastly exceeds that of a state-of-the-art appearance-based method, on a fisheye-camera dataset we collected.