Maximize Peak-to-Sidelobe Ratio for Real-Time RGB-T Tracking

Different from most existing algorithms that explore the integration of information from RGB and thermal (RGB-T) hierarchical features, we propose a novel adaptive learning of modal information from the decision-level perspective to achieve efficient and robust tracking. In our paradigm, the relative reliability between different modalities is mined by maximizing the peak-to-sidelobe ratio (PSR) model. Synchronously, the learned reliability can also be used to guide the correct update of the target template for each modality. Experiments on widely used large-scale benchmarks demonstrate that our method achieves competitive performance against other state-of-the-art trackers while enabling real-time tracking.