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.
This paper presents a novel visual tracking method based on linear representation. First, we present a novel probability continuous outlier model (PCOM) to depict the continuous outliers within the linear representation model. In the proposed model, the element of the noisy observation sample can be either represented by a principle component analysis subspace with small Guassian noise or treated as an arbitrary value with a uniform prior, in which a simple Markov random field model is adopted to exploit the spatial consistency information among outliers (or inliners). Then, we derive the objective function of the PCOM method from the perspective of probability theory. The objective function can be solved iteratively by using the outlier-free least squares and standard max-flow/min-cut steps. Finally, for visual tracking, we develop an effective observation likelihood function based on the proposed PCOM method and background information, and design a simple update scheme. Both qualitative and quantitative evaluations demonstrate that our tracker achieves considerable performance in terms of both accuracy and speed.
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