Multiple Object Tracking in Video SAR: A Benchmark and Tracking Baseline

TL;DR

This paper introduces a new benchmark dataset for multi-object tracking in Video SAR, along with a novel tracking method that enhances robustness against Doppler-induced artifacts and appearance changes.

Contribution

It provides the first public Video SAR MOT benchmark dataset and proposes a novel motion-aware tracking baseline that improves tracking accuracy in challenging conditions.

Findings

Achieved state-of-the-art performance on the VSMB dataset.

Introduced a line feature enhancement mechanism for better target detection.

Developed a motion-aware clue discarding mechanism to improve robustness.

Abstract

In the context of multi-object tracking using video synthetic aperture radar (Video SAR), Doppler shifts induced by target motion result in artifacts that are easily mistaken for shadows caused by static occlusions. Moreover, appearance changes of the target caused by Doppler mismatch may lead to association failures and disrupt trajectory continuity. A major limitation in this field is the lack of public benchmark datasets for standardized algorithm evaluation. To address the above challenges, we collected and annotated 45 video SAR sequences containing moving targets, and named the Video SAR MOT Benchmark (VSMB). Specifically, to mitigate the effects of trailing and defocusing in moving targets, we introduce a line feature enhancement mechanism that emphasizes the positive role of motion shadows and reduces false alarms induced by static occlusions. In addition, to mitigate the adverse effects of target appearance variations, we propose a motion-aware clue discarding mechanism that substantially improves tracking robustness in Video SAR. The proposed model achieves state-of-the-art performance on the VSMB, and the dataset and model are released at https://github.com/softwarePupil/VSMB.