期刊
APPLIED SCIENCES-BASEL
卷 11, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/app11188698
关键词
person following; robust visual tracking; tracking reliability; response fusion; unmanned ground vehicle
类别
资金
- Defense Industrial Technology Development Program [JCKY2019602C015]
The paper proposes a robust visual tracking approach that identifies the target and distractors using a scene analysis module, achieving impressive tracking performance in person tracking.
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.
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