基于运动信息cnn和GM-PHD滤波的卫星视频小目标检测与跟踪

IF 1.3 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Camilo Aguilar, M. Ortner, J. Zerubia
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引用次数: 5

摘要

低分辨率遥感图像中的小目标跟踪存在诸多挑战。与视野相比,目标相对较小,没有明显的特征,并且经常在混乱的环境中丢失。在本文中,我们提出了一种利用卷积神经网络和贝叶斯跟踪器来检测和跟踪小运动目标的跟踪方法。我们的目标检测包括基于运动和基于patch的卷积神经网络(CNN)的两步过程。第一阶段执行轻量级运动检测算子以获得粗略的目标位置。第二阶段使用这些信息与CNN相结合来改进检测结果。此外,我们采用了一种基于检测的在线跟踪方法,利用概率假设密度(PHD)滤波器将检测结果转换为轨迹。PHD过滤器提供了一个健壮的多目标贝叶斯数据关联框架,它在混乱的环境中表现良好,跟踪错过的检测,并且比不同的贝叶斯过滤器表现出显著的计算优势。我们在一个具有挑战性的数据集的各种情况下测试了我们的方法:一个包含许多小移动物体的低分辨率卫星视频。我们证明了所提出的方法在不同场景下的目标检测和目标跟踪指标都优于竞争方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small Object Detection and Tracking in Satellite Videos With Motion Informed-CNN and GM-PHD Filter
Small object tracking in low-resolution remote sensing images presents numerous challenges. Targets are relatively small compared to the field of view, do not present distinct features, and are often lost in cluttered environments. In this paper, we propose a track-by-detection approach to detect and track small moving targets by using a convolutional neural network and a Bayesian tracker. Our object detection consists of a two-step process based on motion and a patch-based convolutional neural network (CNN). The first stage performs a lightweight motion detection operator to obtain rough target locations. The second stage uses this information combined with a CNN to refine the detection results. In addition, we adopt an online track-by-detection approach by using the Probability Hypothesis Density (PHD) filter to convert detections into tracks. The PHD filter offers a robust multi-object Bayesian data-association framework that performs well in cluttered environments, keeps track of missed detections, and presents remarkable computational advantages over different Bayesian filters. We test our method across various cases of a challenging dataset: a low-resolution satellite video comprising numerous small moving objects. We demonstrate the proposed method outperforms competing approaches across different scenarios with both object detection and object tracking metrics.
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