Abstract
Recognizing the motion of Micro Aerial Vehicles (MAVs) is crucial for enabling cooperative perception and control in autonomous aerial swarms. Yet, vision-based recognition models relying only on RGB data often fail to capture the complex spatial temporal characteristics of MAV motion, which limits their ability to distinguish different actions. To overcome this problem, this paper presents MAVR-Net, a multi-view learning-based MAV action recognition framework. Unlike traditional single-view methods, the proposed approach combines three complementary types of data, including raw RGB frames, optical flow, and segmentation masks, to improve the robustness and accuracy of MAV motion recognition. Specifically, ResNet-based encoders are used to extract discriminative features from each view, and a multi-scale feature pyramid is adopted to preserve the spatiotemporal details of MAV motion patterns. To enhance the interaction between different views, a cross-view attention module is introduced to model the dependencies among various modalities and feature scales. In addition, a multi-view alignment loss is designed to ensure semantic consistency and strengthen cross-view feature representations. Experimental results on benchmark MAV action datasets show that our method clearly outperforms existing approaches, achieving 97.8\%, 96.5\%, and 92.8\% accuracy on the Short MAV, Medium MAV, and Long MAV datasets, respectively.
Abstract (translated)
微小飞行器（MAVs）的运动识别对于实现自主空中集群中的协作感知和控制至关重要。然而，仅依赖RGB数据的视觉模型常常无法捕捉到MAV运动复杂的时空特性，这限制了其区分不同动作的能力。为了解决这个问题，本文提出了基于多视图学习的MAV动作识别框架——MAVR-Net。不同于传统的单视图方法，该方法结合了三种互补类型的数据：原始RGB帧、光流和分割掩码，以提高MAV运动识别的鲁棒性和准确性。 具体而言，使用ResNet基元编码器从每个视角提取判别性特征，并采用多尺度特征金字塔来保留MAV运动模式的空间时间细节。为了增强不同视图之间的交互，引入了跨视图注意力模块，用于建模各种模式和特征尺度间的依赖关系。此外，设计了一种多视图对齐损失，以确保语义一致性并强化跨视图特征表示。 在基准MAV动作数据集上的实验结果表明，该方法明显优于现有方法，在Short MAV、Medium MAV和Long MAV数据集上分别实现了97.8%、96.5%和92.8%的准确率。
URL
https://arxiv.org/abs/2510.15448
