Person Re-identification by Online Transfer Learning for Mobile Robot using Region-based CNN and Triplet Loss

Abstract

We propose a person re-identification method for mobile robots that periodically provides services to specific groups. This method consists of a feature extractor that learns to extract person features based on the Triplet Loss from person regions detected by a region-based CNN and a person re-identifier that learns to identify persons through transfer learning of person features while moving around a room. The person re-identification incorporates adaptive transfer learning to periodically re-learn the same persons with different appearance, such as clothes etc. Performance of the proposed method is evaluated by an experiment using a public large-scale data set and an experiment using the self-made dataset periodically collected for the same group by the mobile robot.