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Comparison of two human detection algorithms that apply Bayesian and Neyman-Pearson test criteria using infrared images.

机译：比较两种使用红外图像应用贝叶斯和Neyman-Pearson测试标准的人体检测算法。

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摘要

This thesis focuses on the application of two human detection algorithms that apply Bayesian and Neyman Pearson test criteria technique for human detection on Long Wave Infra Red (LWIR) images in non-urban environment. These two criteria use threshold values in order to separate regions of interest into human and nonhuman. The selected regions of interest from detection are classified using the Support Vector Machine (SVM).;The SVM with different kernel functions (Linear, Quadratic, and Polynomial) will be used to classify the two human detection algorithms. The performance of the classifiers will then be evaluated separately using a performance evaluation curve, Receiver Operating Characteristic (ROC), to find out which kernel is the best performer. The results from the two detection algorithms will then be compared based on their ability to detect humans using LWIR images in non-urban environment.;My proposed contribution applies the Neyman-Pearson criterion test in designing a human detection algorithm that would maximize the probability of detection with constrained probability of false alarm. The humans are detected within a test image by the use of a 50x20 rectangular template containing image intensity data with selected mean and standard deviation as threshold values. The mean and standard deviation threshold values are selected from the ROC curve after the application of the likelihood ratio test with known prior information on training data to classify the data. This rectangular template is used to classify a region as human or non-human by sliding it over a test image. Compared to the detection algorithm by [3] that applies the Bayesian criterion and assume equal prior for human and nonhuman classes and gaussian distribution to derive a threshold expression, the application of the Neyman-Pearson Test Criterion gave a better detection results using our test images from the Jet Propulsion Laboratory.;A total of 160 LWIR images from Jet Propulsion Laboratory (JPL) were used in this thesis work.
机译：本文着重介绍了两种人类检测​​算法的应用，这些算法将贝叶斯和Neyman Pearson测试标准技术用于非城市环境中的长波红外（LWIR）图像上的人类检测。这两个标准使用阈值，以便将关注区域分为人类和非人类。使用支持向量机（SVM）对从检测中选择的感兴趣区域进行分类。具有不同核函数（线性，二次和多项式）的SVM将用于对两种人类检测​​算法进行分类。然后，将使用性能评估曲线接收器工作特性（ROC）分别评估分类器的性能，以找出哪个内核是性能最好的内核。然后将根据这两种检测算法在非城市环境中使用LWIR图像检测人类的能力来比较结果。我的建议是应用Neyman-Pearson准则检验设计能够最大程度地提高检测概率的人类检测算法。误报概率受限的检测。通过使用50x20矩形模板在测试图像中检测到人，该模板包含具有选定平均值和标准差作为阈值的图像强度数据。在应用似然比测试后，利用训练数据上的已知先验信息从ROC曲线中选择平均值和标准偏差阈值，以对数据进行分类。通过在测试图像上滑动此矩形模板，可以将区域分为人类区域或非人类区域。与采用贝叶斯准则并假设人类和非人类类别具有相同先验且采用高斯分布来导出阈值表达式的[3]的检测算法相比，Neyman-Pearson测试准则的应用在我们的测试图像中给出了更好的检测结果；来自喷气推进实验室（JPL）的160幅LWIR图像被用于本论文工作。

著录项

  • 作者

    Laryea, Henry.;

  • 作者单位

    Howard University.;

  • 授予单位 Howard University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.E.
  • 年度 2008
  • 页码 111 p.
  • 总页数 111
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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