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首页> 外文会议>Conference on unattended ground, sea and air sensor technologies and applications XI; 20090413-16; Orlando, FL(US) >Classification of humans and animals using an infrared profiling sensor
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Classification of humans and animals using an infrared profiling sensor

机译：使用红外轮廓传感器对人和动物进行分类

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摘要

This paper presents initial object profile classification results using range and elevation independent features from a simulated infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. A field data collection effort to yield profiles of humans and animals is reported. Range and elevation independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test four classification algorithms to classify objects as humans or animals. The performance of Naive Bayesian (NB), Naive Bayesian with Linear Discriminant Analysis (LDA+NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set SVM and (LDA+NB) are capable of providing classification rates as high as 98.5%. For perimeter security applications where misclassification of humans as animals (true negatives) needs to be avoided, SVM and NB provide true negative rates of 0% while maintaining overall classification rates of over 95%.
机译：本文介绍了使用模拟红外轮廓传感器提供的与距离和仰角无关的特征进行初始物体轮廓分类的结果。使用LWIR摄像机模拟了被动红外轮廓传感器。据报道，实地数据收集工作产生了人类和动物的概况。从轮廓中提取基于对象高度和宽度的与范围和仰角无关的特征。然后使用轮廓特征来训练和测试四种分类算法，以将物体分类为人或动物。基于分类准确度，比较了朴素贝叶斯（NB），具有线性判别分析的朴素贝叶斯（LDA + NB），K最近邻（K-NN）和支持向量机（SVM）的性能。结果表明，对于我们的数据集，SVM和（LDA + NB）能够提供高达98.5％的分类率。对于需要避免将人类错误分类为动物（真阴性）的外围安全应用程序，SVM和NB提供的真阴性率为0％，而总分类率却保持在95％以上。

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