Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection
For UAV-based object detection, this work addresses the challenge of severe ego-motion and jitter, but the improvements are incremental over existing methods.
The paper proposes a motion-guided detection framework for UAVs that decouples target motion from ego-motion using homography-based global motion compensation and dual-interval motion extraction, achieving consistent improvements over a YOLOv8 baseline on VisDrone-VID.
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.