Progressive Domain Adaptation for Robot Vision Person Re-identification

Zijun Sha, Zelong Zeng, Zheng Wang, Yoichi Natori, Yasuhiro Taniguchi, Shin'ichi Satoh

Abstract: Person re-identification has received much attention in the last few years, as it enhances the retrieval effectiveness in the video surveillance networks and video archive management. In this paper, we demonstrate a guiding robot with person followers system, which recognizes the follower using a person re-identification technology. It first adopts existing face recognition and person tracking methods to generate person tracklets with different IDs. Then, a classic person re-identification model, pre-trained on the surveillance dataset, is adapted to the new robot vision condition incrementally. The demonstration showcases the quality of robot follower focusing.