  Benefit from large-scale training data, recent advances in Siamese-based
object tracking have achieved compelling results on the normal sequences.
Whilst Siamese-based trackers assume training and test data follow an identical
distribution. Suppose there is a set of foggy or rainy test sequences, it
cannot be guaranteed that the trackers trained on the normal images perform
well on the data belonging to other domains. The problem of domain shift among
training and test data has already been discussed in object detection and
semantic segmentation areas, which, however, has not been investigated for
visual tracking. To this end, based on SiamRPN++, we introduce a Domain
Adaptive SiamRPN++, namely DASiamRPN++, to improve the cross-domain
transferability and robustness of a tracker. Inspired by A-distance theory, we
present two domain adaptive modules, Pixel Domain Adaptation (PDA) and Semantic
Domain Adaptation (SDA). The PDA module aligns the feature maps of template and
search region images to eliminate the pixel-level domain shift caused by
weather, illumination, etc. The SDA module aligns the feature representations
of the tracking target's appearance to eliminate the semantic-level domain
shift. PDA and SDA modules reduce the domain disparity by learning domain
classifiers in an adversarial training manner. The domain classifiers enforce
the network to learn domain-invariant feature representations. Extensive
experiments are performed on the standard datasets of two different domains,
including synthetic foggy and TIR sequences, which demonstrate the
transferability and domain adaptability of the proposed tracker.
