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

• URL: http://arxiv.org/abs/2007.02041v1
• Date: Sat, 4 Jul 2020 08:11:33 GMT
• Title: Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
• Authors: Pengyu Zhang and Jie Zhao and Dong Wang and Huchuan Lu and Xiaoyun Yang
• Abstract summary: We develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. When the appearance cue is unreliable, we take motion cues into account to make the tracker robust. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.
• Score: 85.333260415532
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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