  Tracking objects can be a difficult task in computer vision, especially when
faced with challenges such as occlusion, changes in lighting, and motion blur.
Recent advances in deep learning have shown promise in challenging these
conditions. However, most deep learning-based object trackers only use visible
band (RGB) images. Thermal infrared electromagnetic waves (TIR) can provide
additional information about an object, including its temperature, when faced
with challenging conditions. We propose a deep learning-based image tracking
approach that fuses RGB and thermal images (RGBT). The proposed model consists
of two main components: a feature extractor and a tracker. The feature
extractor encodes deep features from both the RGB and the TIR images. The
tracker then uses these features to track the object using an enhanced
attribute-based architecture. We propose a fusion of attribute-specific feature
selection with an aggregation module. The proposed methods are evaluated on the
RGBT234 \cite{LiCLiang2018} and LasHeR \cite{LiLasher2021} datasets, which are
the most widely used RGBT object-tracking datasets in the literature. The
results show that the proposed system outperforms state-of-the-art RGBT object
trackers on these datasets, with a relatively smaller number of parameters.
