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

2025-06-13

AI Summary

Video SAR multi-object tracking faces three key challenges: Doppler-induced artifacts from moving targets are easily confused with static shadows; Doppler mismatch distorts target appearance, degrading trajectory association; and the lack of public benchmarks hinders algorithm evaluation. To address these, we introduce VSMB—the first open-source video SAR multi-object tracking benchmark, comprising 45 real-world sequences. We propose a line-feature enhancement mechanism that transforms motion-induced shadows into geometrically consistent cues, and a motion-aware feature dropout mechanism that adaptively suppresses unreliable appearance features corrupted by Doppler effects, thereby improving association robustness. Our method integrates Doppler modeling, geometric line-feature extraction, motion-state-guided association filtering, and an end-to-end trainable framework. Evaluated on VSMB, our approach achieves state-of-the-art performance. Both the code and dataset are fully open-sourced to foster standardization and reproducible research in video SAR tracking.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Doppler shifts cause artifacts mistaken for static shadows
• Appearance changes from Doppler mismatch disrupt trajectory continuity
• Lack of public benchmark datasets for Video SAR tracking

Innovation

• Line feature enhancement reduces false alarms
• Motion-aware clue discarding improves robustness
• Video SAR MOT Benchmark dataset released