Bayesian Dumbbell Diffusion Model for RGBT Object Tracking With Enriched Priors
Shenghua Fan, Chu He, Chenxia Wei, Yujin Zheng, Xi Chen
Abstract
RGBT tracking can be accomplished by constructing Bayesian estimators that incorporate fusion prior distributions for the visible (RGB) and thermal (T) modalities. Such estimators enable the computation of a posterior distribution for the variables of interest to locate the target. Incorporating rich prior information can improve the performance of predictors. However, current RGBT trackers face limited fusion prior data. To mitigate this issue, we propose a novel tracker, BD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Track, which employs a diffusion model. Firstly, this paper introduces a dumbbell diffusion model, and employ convolution networks and the dumbbell model to derive the fusion feature prior information from various index frames in the same tracking video sequence. Secondly, we propose a plug-and-play channel augmented joint learning strategy to derive the images prior distribution. This strategy not only homogeneously generates modality-relevant prior information but also increases the distance between positive and negative samples within the modality, while reducing the distance between modalities during fusion. Results demonstrate promising performance in the GTOT, RGBT234, LasHeR, and VTUAV-ST datasets, surpassing other state-of-the-art trackers.