一种新的AMS-DAT卫星视频中移动车辆检测算法

IF 4 3区 地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu
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引用次数: 3

摘要

卫星视频最近已成为交通管理和军事监视中广泛应用的新数据源。卫星视频由于覆盖范围更广,在大规模监控中比地面监控视频更有优势。然而,伪运动背景和低分辨率目标给卫星视频中的运动车辆检测带来了新的挑战,导致传统的目标检测方法在卫星视频中的应用效果不佳。为了克服这一困难,我们提出了一种基于自适应运动分离和差分累积轨迹的运动车辆检测方法。具体来说,考虑到卫星视频中车辆的尺度不变性,设计了一种新的指标来辅助运动目标和背景的自适应分离。同时,提出了一种基于差分累积轨迹的车辆识别算法,用于从伪运动背景中区分运动车辆。在两个卫星视频数据集上的实验结果表明,该方法比目前最先进的移动车辆检测方法具有更好的检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video
Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.
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来源期刊
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理
CiteScore
7.60
自引率
12.50%
发文量
1113
审稿时长
3.4 months
期刊介绍: IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.
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