  Visual object tracking with the visible (RGB) and thermal infrared (TIR)
electromagnetic waves, shorted in RGBT tracking, recently draws increasing
attention in the tracking community. Considering the rapid development of deep
learning, a survey for the recent deep neural network based RGBT trackers is
presented in this paper. Firstly, we give brief introduction for the RGBT
trackers concluded into this category. Then, a comparison among the existing
RGBT trackers on several challenging benchmarks is given statistically.
Specifically, MDNet and Siamese architectures are the two mainstream frameworks
in the RGBT community, especially the former. Trackers based on MDNet achieve
higher performance while Siamese-based trackers satisfy the real-time
requirement. In summary, since the large-scale dataset LasHeR is published, the
integration of end-to-end framework, e.g., Siamese and Transformer, should be
further considered to fulfil the real-time as well as more robust performance.
Furthermore, the mathematical meaning should be more considered during
designing the network. This survey can be treated as a look-up-table for
researchers who are concerned about RGBT tracking.
