# Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR

arXiv: https://arxiv.org/abs/2211.15995 · HTML: https://mlsanity.com/paper/2211.15995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2022.

## Built on by (most cited first, 1 of 1)

- [RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification](https://mlsanity.com/paper/2408.17143/) (2024)

## Abstract (arXiv)

This work focuses on multi-target tracking in Video synthetic aperture radar. Specifically, we refer to tracking based on targets' shadows. Current methods have limited accuracy as they fail to consider shadows' characteristics and surroundings fully. Shades are low-scattering and varied, resulting in missed tracking. Surroundings can cause interferences, resulting in false tracking. To solve these, we propose a shadow-oriented multi-target tracking method (SOTrack). To avoid false tracking, a pre-processing module is proposed to enhance shadows from surroundings, thus reducing their interferences. To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation. And further, a recall module is designed to recall missed shadows. We conduct experiments on measured data. Results demonstrate that, compared with other methods, SOTrack achieves much higher performance in tracking accuracy-18.4%. And ablation study confirms the effectiveness of the proposed modules.