BiMPeR: A Novel Bi-Model Person Re-identification Method based on the Appearance and the Gait Features

Abstract

Person re-identification presents an active research area for intelligent video surveillance systems. The purpose is to find the same person from disjoint camera views at different times and locations. In this paper, we propose a novel Bi-Model Person Re-identification method (BiMPeR) that combines the appearance and the gait features to improve the re-identification performance and to handle the problem of similar appearances. The main idea is to prove the complementarity of these two modalities to extract a discriminative person signature for the re-identification problem. A score fusion method was adopted to combine these two modalities to reflect the impact of each one on the final decision. Experiments were performed on the CASIA-B database revealing promising results and showing the effectiveness of the proposed method against state-of-the-art uni-model methods.