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Performance comparison of feature extraction algorithms for target detection and classification

机译:用于目标检测和分类的特征提取算法的性能比较

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摘要

This paper addresses the problem of target detection and classification, where the performance is often limited due to high rates of false alarm and classification error, possibly because of inadequacies in the underlying algorithms of feature extraction from sensory data and subsequent pattern classification. In this paper, a recently reported feature extraction algorithm, symbolic dynamic filtering (SDF), is investigated for target detection and classification by using unmanned ground sensors (UGS). In SDF, sensor time series data are first symbolized to construct probabilistic finite state automata (PFSA) that, in turn, generate low-dimensional feature vectors. In this paper, the performance of SDF is compared with that of two commonly used feature extractors, namely Cepstrum and principal component analysis (PCA), for target detection and classification. Three different pattern classifiers have been employed to compare the performance of the three feature extractors for target detection and human/animal classification by UGS systems based on two sets of field data that consist of passive infrared (PIR) and seismic sensors. The results show consistently superior performance of SDF-based feature extraction over Cepstrumbased and PCA-based feature extraction in terms of successful detection, false alarm, and misclassification rates.
机译:本文解决了目标检测和分类的问题,由于错误警报和分类错误的发生率很高,因此性能通常受到限制,这可能是由于从感官数据中提取特征和后续模式分类的基础算法的不足所致。在本文中,研究了一种最近报告的特征提取算法,即符号动态滤波(SDF),它通过使用无人地面传感器(UGS)进行目标检测和分类。在SDF中,首先对传感器时间序列数据进行符号化,以构造概率有限状态自动机(PFSA),然后自动生成低维特征向量。在本文中,将SDF的性能与两个常用的特征提取器(倒谱和主成分分析(PCA))的性能进行了比较,以进行目标检测和分类。三种不同的模式分类器已被用于比较UGS系统基于两组由被动红外(PIR)和地震传感器组成的现场数据进行目标检测和人/动物分类的三个特征提取器的性能。结果表明,在成功检测,误报和误分类率方面,基于SDF的特征提取始终优于基于倒谱和基于PCA的特征提取。

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