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arXiv · 2007.02041

Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking

Pengyu Zhang·Jie Zhao·Dong Wang·Huchuan Lu·Xiaoyun Yang
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Abstract

In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues. First, to obtain a robust appearance model, we develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. The fusion weights are determined by using offline-trained global and local multimodal fusion networks, and then adopted to linearly combine the response maps of RGB and T modalities. Second, when the appearance cue is unreliable, we comprehensively take motion cues, i.e., target and camera motions, into account to make the tracker robust. We further propose a tracker switcher to switch the appearance and motion trackers flexibly. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.

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Pengyu Zhang, Jie Zhao, Dong Wang, Huchuan Lu, Xiaoyun Yang. 2020-07-04. Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking. https://arxiv.org/abs/2007.02041

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