Multi-templates based robust tracking for robot person-following tasks

Minghe Cao, Jianzhong Wang*, Li Ming

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

While the robotics techniques have not developed to full automation, robot following is common and crucial in robotic applications to reduce the need for dedicated teleoperation. To achieve this task, the target must first be robustly and consistently perceived. In this paper, a robust visual tracking approach is proposed. The approach adopts a scene analysis module (SAM) to identify the real target and similar distractors, leveraging statistical characteristics of cross-correlation responses. Positive templates are collected based on the tracking confidence constructed by the SAM, and negative templates are gathered by the recognized distractors. Based on the collected templates, response fusion is performed. As a result, the responses of the target are enhanced and the false responses are suppressed, leading to robust tracking results. The proposed approach is validated on an outdoor robot-person following dataset and a collection of public person tracking datasets. The results show that our approach achieved state-of-the-art tracking performance in terms of both the robustness and AUC score.

源语言英语
文章编号8698
期刊Applied Sciences (Switzerland)
11
18
DOI
出版状态已出版 - 9月 2021

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