Drone Detection Based on FD-HOG Descriptor

Zizhe Wang, L. Qi, Tie Yun, Yi Ding, Yang Bai
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引用次数: 9

Abstract

The rapid development of Unmanned Aerial Vehicle (UAV) technology, also known as drones, has made people benefit in many ways, but it also brings privacy and security issues, such as appeared at private place, airports, prisons, etc. Therefore, the detection of drones in a specific area is crucial. Video detection is an effective method with various advantages. In this paper, we have used the background subtraction method to detect the moving object in the video sequence which recorded by static cameras, then extracted the global Fourier descriptors and the local HOG features of the moving object images. Finally, the FD+HOG features have been sent to the SVM classifier for classification and recognition. Our algorithm is simple and efficient, an overall with 98% accuracy was obtained in our data set.
基于FD-HOG描述符的无人机检测
无人驾驶飞行器(UAV)技术的飞速发展,在给人们带来诸多好处的同时,也带来了隐私和安全问题,如出现在私人场所、机场、监狱等。因此,在特定区域检测无人机至关重要。视频检测是一种有效的检测方法,具有多种优点。本文采用背景减法检测静态摄像机记录的视频序列中的运动目标,提取运动目标图像的全局傅里叶描述子和局部HOG特征。最后将FD+HOG特征发送给SVM分类器进行分类识别。我们的算法简单高效,在我们的数据集上获得了98%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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