  Planar object tracking plays an important role in AI applications, such as
robotics, visual servoing, and visual SLAM. Although the previous planar
trackers work well in most scenarios, it is still a challenging task due to the
rapid motion and large transformation between two consecutive frames. The
essential reason behind this problem is that the condition number of such a
non-linear system changes unstably when the searching range of the homography
parameter space becomes larger. To this end, we propose a novel Homography
Decomposition Networks(HDN) approach that drastically reduces and stabilizes
the condition number by decomposing the homography transformation into two
groups. Specifically, a similarity transformation estimator is designed to
predict the first group robustly by a deep convolution equivariant network. By
taking advantage of the scale and rotation estimation with high confidence, a
residual transformation is estimated by a simple regression model. Furthermore,
the proposed end-to-end network is trained in a semi-supervised fashion.
Extensive experiments show that our proposed approach outperforms the
state-of-the-art planar tracking methods at a large margin on the challenging
POT, UCSB and POIC datasets.
