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

TL;DR

This paper introduces SOTrack, a shadow-oriented multi-target tracking method for Video-SAR that enhances shadow detection and reduces interference, significantly improving tracking accuracy over existing methods.

Contribution

The paper presents a novel shadow-oriented tracking approach with a pre-processing enhancement, deep learning-based shadow detection, and a recall module, improving accuracy in Video-SAR multi-target tracking.

Findings

Achieves 18.4% higher tracking accuracy compared to other methods.

Effective shadow enhancement reduces false tracking.

Deep learning-based shadow detection improves missed shadow recall.

Abstract

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.