MotionNet-PGA: MotionNet with Polar-Guided Attention for Moving Object Segmentation in Scanning Radar

Abstract

Moving object segmentation (MOS) is essential for autonomous driving, enabling robust detection, tracking, and prediction of dynamic agents in complex traffic scenarios. Radar sensors offer notable advantages for long-range sensing, but their lower spatial resolution, measurement noise, and geometric distortions—particularly for distant targets—pose significant challenges for accurate MOS. These limitations are amplified when detecting small objects such as scooters. In this work, we present MotionNet-PGA, a Polar-guided Attention Framework designed specifically for scanning radar-based MOS. Our method builds on the multi-frame motion encoding backbone of MotionNet, and introduces a polar-guided attention module to suppress clutter, enhance motion feature representation, and improve segmentation of small and distant targets. For evaluation, we construct and annotate the ITRI Radar moving object segmentation Dataset. Experimental results demonstrate that our method surpasses state-of-the-art baseline, MotionNet, by 2.48% in overall IoU and achieves a 4.08% improvement in small-object segmentation. These results highlight the effectiveness of polar-guided attention in addressing scanning radar-specific challenges.