Aggregation Signature for Small Object Tracking

TL;DR

This paper introduces a novel aggregation signature descriptor for small object tracking, addressing challenges of appearance variability and object loss, and demonstrates superior performance on new benchmark datasets.

Contribution

It proposes a new saliency-based aggregation signature for small objects, proves its accuracy in matching foreground objects, and provides new datasets for small object tracking.

Findings

Outperforms state-of-the-art methods on multiple datasets.

Achieves high accuracy in representing small objects.

Introduces two new benchmark datasets, small90 and small112.

Abstract

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/.