Low-quality modalities contain not only a lot of noisy information but also
some discriminative features in RGBT tracking. However, the potentials of
low-quality modalities are not well explored in existing RGBT tracking
algorithms. In this work, we propose a novel duality-gated mutual condition
network to fully exploit the discriminative information of all modalities while
suppressing the effects of data noise. In specific, we design a mutual
condition module, which takes the discriminative information of a modality as
the condition to guide feature learning of target appearance in another
modality. Such module can effectively enhance target representations of all
modalities even in the presence of low-quality modalities. To improve the
quality of conditions and further reduce data noise, we propose a duality-gated
mechanism and integrate it into the mutual condition module. To deal with the
tracking failure caused by sudden camera motion, which often occurs in RGBT
tracking, we design a resampling strategy based on optical flow algorithms. It
does not increase much computational cost since we perform optical flow
calculation only when the model prediction is unreliable and then execute
resampling when the sudden camera motion is detected. Extensive experiments on
four RGBT tracking benchmark datasets show that our method performs favorably
against the state-of-the-art tracking algorithms

我们提出了一种新颖的双重门互条件网络，以充分利用所有模态的判别信息，同时抑制数据噪声的影响，并通过光流算法设计重新采样策略来应对 RGBT 跟踪中的突然相机运动造成的跟踪失败。