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Abstract
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.
Highlights
While the robotics techniques have not led to full automation, human–robot collaboration scenarios have arisen in diverse domains, such as manufacturing, health care, and entertainment
Our main contributions can be summarized as follows: (1) We proposed a tracking reliability criterion based on the variance of center responses
Since the SiamRPN-based approaches regress bounding boxes from pre-defined anchors, the size adjustment is minor; we demonstrate that the robustness criterion will not be biased by the large-area bounding boxes
Summary
The tracking performance using traditional features is severely restricted when tracking scenarios are complex. The correlation can be seen as a similarity calculation, and the response map reflects the similarity between the template and the search region Following this similarity-learning work, Li et al propose SiamRPN [14], which enhances the tracking performance by integrating a region proposal network (RPN) into SiamFC. LTMU [23] proposed a meta-updater that guides the tracker update, forming a long-term tracking framework along with an online local tracker, an online verifier, and a SiamRPN-based re-detector These methods significantly improved tracking precision but have a low tracking frame rate even in high-end desktops. Zhang et al [26] deployed local–global multiple correlation filters for tracking and a Kalman filter re-detection model for re-detection when the correlation filters are unreliable Methods such as online updater, re-detection module, hierarchical search, and multi-stage framework are commonly used to handle tracking robustness issues in long-term tracking. By fusing the scores of the templates, the target responses are enhanced and noises are suppressed, leading to robust tracking
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