  Deep learning-based methods monopolize the latest research in the field of
thermal infrared (TIR) object tracking. However, relying solely on deep
learning models to obtain better tracking results requires carefully selecting
feature information that is beneficial to representing the target object and
designing a reasonable template update strategy, which undoubtedly increases
the difficulty of model design. Thus, recent TIR tracking methods face many
challenges in complex scenarios. This paper introduces a novel Deep Bayesian
Filtering (DBF) method to enhance TIR tracking in these challenging situations.
DBF is distinctive in its dual-model structure: the system and observation
models. The system model leverages motion data to estimate the potential
positions of the target object based on two-dimensional Brownian motion, thus
generating a prior probability. Following this, the observation model comes
into play upon capturing the TIR image. It serves as a classifier and employs
infrared information to ascertain the likelihood of these estimated positions,
creating a likelihood probability. According to the guidance of the two models,
the position of the target object can be determined, and the template can be
dynamically updated. Experimental analysis across several benchmark datasets
reveals that DBF achieves competitive performance, surpassing most existing TIR
tracking methods in complex scenarios.
