# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
###### 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. person following; robust visual tracking; tracking reliability; response fusion; unmanned ground vehicle +
Footnote †: journal: Applied Sciences  
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Footnote 1: School of Mechanical Engineering, Beijing 100081, China; [EMAIL_ADDRESS]  
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traditional features can achieve person tracking under certain circumstances but cannot work well under long-term and complex environments. Recently, Siamese-based approaches that adopt discriminative correlation of deep features have been proposed to address these issues. In Siamese-based trackers, it is common (some stated in the papers but not implemented in the released code) to only use the first frame as the template to grant template reliability, which achieved good performance in short-term datasets such as OTB [11] and VOT [12]. However, in experiments, using a fixed template can perform well for a certain duration, but over time, the variations in appearance, illumination, scale, deformation, etc., reduce the intensity of the responses to the tracked target, and eventually, tracking is lost. The intuitive solution to this issue is to continue incorporating the latest target information, but [PERSON] et al. [13] proved that the tracking performance only worsens if a non-discriminative template update strategy is applied throughout the tracking process. We think the reason for that is the introduction of false-positive templates. Therefore, a tracking reliability criterion is needed to safely incorporate new templates. Providing new target information only enhances the responses of target tracking, but false responses are still caused by similar objects. As illustrated in the ground truth score map row of Figure 1, even though the people have different appearances, they receive high responses in the score map. When the real tracking target crosses or is occluded by these objects, the tracking is easily lost with these interferences. It is also important to eliminate false responses. Motivated by the aforementioned analysis, we propose a robust tracking approach to enable robot person-following tasks. A scene analysis module (SAM) is proposed that leverages the statistical characteristics of the cross-correlation responses. The density distribution of the responses is estimated using a Gaussian mixture model. Based on the mutual information of the mixture components, the responses are segmented into instance-aware clusters. As a result, a tracking reliability criterion is proposed based on the size of the center cluster, and distractors that produce false responses are extracted as negative templates. By collecting the positive and negative templates, a  
Figure 1: Illustration of our score fusion procedure. The ground truth score map not only responds to the real target but also raises the responses in many other areas where the tracker is easily misled. After implementing fusion, the unrelated responses are sufficiently suppressed.