This paper presents a novel method for localization and recognition of moving objects in a real airport surface scene. Different from the traditional applications, moving object detection (MOD) in the airport surface is more challenging because the background is an open outdoor environment, which means that the target objects are usually low in resolution and the MOD task is vulnerable to many undesired changes, such as cloud movement and illumination variations. To address these issues, this paper proposes a unified and effective deep-learning-based MOD architecture, which combines both appearance and motion cues. Specifically, a novel moving region proposal generation module is first designed, which can effectively locate the regions of moving object based on the motion information. Meanwhile, a novel cascade multilayer feature fusion module with transposed convolution is applied to produce both enriched-semantics and fine-resolution convolutional feature maps for category recognition. Finally, a large-scale dataset acquired by the daily surveillance videos of a real airport surface is manually constructed. Results show that the proposed methods outperform state-of-the-art solutions in extracting moving objects from airport surface scenes.

本文提出了一种在真实机场表面场景中定位和识别运动物体的新方法。与传统应用不同，机场表面的运动物体检测（MOD）更具挑战性，因为背景是开放的室外环境，这意味着目标物体的分辨率通常较低，并且 MOD 任务容易受到许多不良变化的影响，例如云运动和光照变化。为了解决这些问题，本文提出了一种统一且有效的基于深度学习的 MOD 架构，该架构结合了外观和运动线索。具体而言，首先设计了一种新颖的运动区域提议生成模块，该模块可以根据运动信息有效地定位运动对象的区域。同时，应用具有转置卷积的新型级联多层特征融合模块来生成用于类别识别的丰富语义和精细分辨率卷积特征图。最后，通过人工构建真实机场地面日常监控视频获取的大规模数据集。结果表明，所提出的方法在从机场表面场景中提取运动物体方面优于最先进的解决方案。