Aggregation Signature for Small Object Tracking
Chunlei Liu, Wenrui Ding, Jinyu Yang, Vittorio Murino, Baochang Zhang,, Jungong Han, Guodong Guo

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
