Motion-guided small MAV detection in complex and non-planar scenes

TL;DR

This paper introduces a novel motion-guided detection method for small MAVs in complex, non-planar scenes, combining motion features, multi-object tracking, and appearance classification to improve detection accuracy and reduce false positives.

Contribution

The paper presents a new integrated approach that enhances small MAV detection by combining motion feature enhancement, trajectory filtering, and appearance-based classification, outperforming existing methods.

Findings

High detection accuracy on ARD-MAV dataset

Effective in complex and dynamic backgrounds

Outperforms state-of-the-art detection methods

Abstract

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