# Aggregation Signature for Small Object Tracking

arXiv: https://arxiv.org/abs/1910.10859 · HTML: https://mlsanity.com/paper/1910.10859/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Tiny Object Tracking: A Large-scale Dataset and A Baseline](https://mlsanity.com/paper/2202.05659/) (2022)
- [Tracking Small and Fast Moving Objects: A Benchmark](https://mlsanity.com/paper/2209.04284/) (2022)
- [COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking](https://mlsanity.com/paper/2504.01321/) (2025)
- [MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results](https://mlsanity.com/paper/2507.12832/) (2025)
- [Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency Transfer](https://mlsanity.com/paper/2308.14332/) (2023)
- [Small Object Tracking in LiDAR Point Cloud: Learning the Target-awareness Prototype and Fine-grained Search Region](https://mlsanity.com/paper/2401.13285/) (2024)
- [Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection](https://mlsanity.com/paper/2407.07289/) (2024)

## Abstract (arXiv)

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/.