Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection

TL;DR

This paper presents a novel vision-only detection framework for UAVs that effectively decouples target motion from camera disturbances using dual-interval motion cues and a motion-guided attention mechanism.

Contribution

It introduces a dual-interval motion extraction strategy and a lightweight attention module to improve UAV object detection under severe ego-motion conditions.

Findings

Significant performance gains over YOLOv8 baseline on VisDrone-VID dataset.

Effective decoupling of target motion from camera-induced disturbances.

Validation of dual-interval and attention mechanisms through ablation studies.

Abstract

Object detection from Unmanned Aerial Vehicles (UAVs) is challenged by severe ego-motion, camera jitter, and large scale variations. While modern detectors perform well on static images, their direct application to UAV video often fails, particularly for small objects in dynamic scenes. Existing motion-based methods either rely on computationally expensive optical flow or use single-interval differencing, which is sensitive to jitter and limited in capturing diverse motion patterns. We propose a vision-only motion-guided detection framework that decouples target motion from camera-induced disturbances. A homography-based Global Motion Compensation (GMC) first aligns adjacent frames. We then introduce a Dual-Interval Motion Extraction strategy that captures both short-term and long-term motion cues. To integrate these cues, a lightweight Motion-Guided Attention (MGA) module enhances feature representations within a Feature Pyramid Network. Experiments on the VisDrone-VID dataset demonstrate consistent improvements over a strong YOLOv8 baseline under severe ego-motion. Ablation studies further confirm the effectiveness of the dual-interval design and the proposed motion-guided attention mechanism.