Multi-view Person Re-identification in a Fisheye Camera Network with Different Viewing Directions

Abstract

Person re-identification is a crucial component for multi-camera networks in different real-world applications such as surveillance, automation, and business analytics. Despite considerable recent progress, the performance in practice is still not satisfactory due to the high intra-person variation and significant complexity of the task, including differences in scale, viewing direction, and illumination. We propose a novel approach for person re-identification, which exploits multi-view information of a fisheye camera looking downwards from the ceiling. To handle this highly variable multi-view information, we build a generic pipeline for processing fisheye camera imagery based on geometric sensor modelling and deep learning. The proposed approach is evaluated on a re-mapped version of the publicly available Market-1501 dataset, and, in addition, on a new fisheye dataset. Significant improvements are shown in our experiments: our approach achieves more than 97% rank#1 recognition rate if applied on the re-mapped Market-1501 dataset; on the new fisheye dataset we find an improvement of about 12% compared to random-view fusion.