Papers › Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
Pengyu Zhang, Jie Zhao, Dong Wang, Huchuan Lu, Xiaoyun Yang
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Rgb-T Tracking | GTOT | JMMAC | Precision | 90.2 | #11 of 15 | Archive leaderboard | report |
| Rgb-T Tracking | GTOT | JMMAC | Success | 73.2 | #11 of 15 | Archive leaderboard | report |
| Rgb-T Tracking | RGBT234 | JMMAC | Precision | 79.0 | #38 of 42 | Archive leaderboard | report |
| Rgb-T Tracking | RGBT234 | JMMAC | Success | 57.3 | #38 of 42 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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