Abstract. Correlation filters based trackers rely on a periodic
assumption of the search sample to efficiently distinguish the target from
the background. This assumption however yields undesired boundary
effects and restricts aspect ratios of search samples. To handle these
issues, an end-to-end deep architecture is proposed to incorporate
geometric transformations into a correlation filters based network.
This architecture introduces a novel spatial alignment module, which
provides continuous feedback for transforming the target from the border
to the center with a normalized aspect ratio. It enables correlation
filters to work on well-aligned samples for better tracking. The whole
architecture not only learns a generic relationship between object
geometric transformations and object appearances, but also learns robust
representations coupled to correlation filters in case of various geometric
transformations. This lightweight architecture permits real-time speed.
Experiments show our tracker effectively handles boundary effects and
aspect ratio variations, achieving state-of-the-art tracking results on
recent benchmarks