Motion-guided small MAV detection in complex and non-planar scenes
This addresses the challenge of small MAV detection for applications like surveillance and security, but it is incremental as it builds on existing motion and appearance features.
The paper tackles the problem of detecting small micro aerial vehicles (MAVs) in complex and non-planar scenes by proposing a motion-guided detector that aggregates motion features and uses tracking and appearance-based methods to reduce false positives, achieving high performance and outperforming state-of-the-art methods on the ARD-MAV dataset.
In recent years, there has been a growing interest in the visual detection of micro aerial vehicles (MAVs) due to its importance in numerous applications. However, the existing methods based on either appearance or motion features encounter difficulties when the background is complex or the MAV is too small. In this paper, we propose a novel motion-guided MAV detector that can accurately identify small MAVs in complex and non-planar scenes. This detector first exploits a motion feature enhancement module to capture the motion features of small MAVs. Then it uses multi-object tracking and trajectory filtering to eliminate false positives caused by motion parallax. Finally, an appearance-based classifier and an appearance-based detector that operates on the cropped regions are used to achieve precise detection results. Our proposed method can effectively and efficiently detect extremely small MAVs from dynamic and complex backgrounds because it aggregates pixel-level motion features and eliminates false positives based on the motion and appearance features of MAVs. Experiments on the ARD-MAV dataset demonstrate that the proposed method could achieve high performance in small MAV detection under challenging conditions and outperform other state-of-the-art methods across various metrics