  Small object tracking becomes an increasingly important task, which however
has been largely unexplored in computer vision. The great challenges stem from
the facts that: 1) small objects show extreme vague and variable appearances,
and 2) they tend to be lost easier as compared to normal-sized ones due to the
shaking of lens. In this paper, we propose a novel aggregation signature
suitable for small object tracking, especially aiming for the challenge of
sudden and large drift. We make three-fold contributions in this work. First,
technically, we propose a new descriptor, named aggregation signature, based on
saliency, able to represent highly distinctive features for small objects.
Second, theoretically, we prove that the proposed signature matches the
foreground object more accurately with a high probability. Third,
experimentally, the aggregation signature achieves a high performance on
multiple datasets, outperforming the state-of-the-art methods by large margins.
Moreover, we contribute with two newly collected benchmark datasets, i.e.,
small90 and small112, for visually small object tracking. The datasets will be
available in https://github.com/bczhangbczhang/.
