Robust Person Re-Identification for Service Robots Via One-Class Body-Part Transformer and Continual Learning

Abstract

This work presents a robust person tracking and re-identification system designed for Human-Robot Interaction applications. The approach introduces the One-Class Body-Part (OCBP) Transformer, trained online to model interactions among body-part features and construct a robust target representation. To improve data association and reduce identity swaps during the tracking phase, the SORT tracker is extended with depth information in order to provide correct samples for the Online Continual Learning (OCL) setting. The transformer is further enhanced through the use of pseudo-negative samples, which accelerate convergence during the online learning phase. Ablation studies compare the performance of the memory management system using different sample insertion configurations and highlight the benefit of using pseudo-negative samples. The proposed method is evaluated on a public dataset, where it outperforms state-of-the-art approaches in challenging scenarios, and is validated in a real-world person-following experiment with a robotic platform in an environment with multiple distractors, occlusions, out-of-view situations and illumination changes. Despite these complexities, the robot consistently re-identified and followed the target individual. Runtime analysis demonstrates that the system operates reliably on embedded computing platforms with NVIDIA GPUs, making it both robust and resource-efficient for real-world deployment.