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首页> 外文期刊>Asian Journal of Engineering, Sciences & Technology >Unconcealed Gun Detection using Haar-like and HOG Features - A Comparative Approach
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Unconcealed Gun Detection using Haar-like and HOG Features - A Comparative Approach

机译：使用类似Haar和HOG的特征进行隐蔽的枪支检测-比较方法

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Due to its wide variety of applications, object detection has been the center of attention for researchers in the field of digital image processing and computer vision. When trained with the sample training dataset, various object classifiers can detect and classify the objects with prominent accuracy and precision. The major step in any of the object classification algorithm is feature selection. Performance of the classifier depends on robustness of the feature vector selected. This paper presents unconcealed gun detection method by using Boosted Cascade Classifier. The classifier was trained with two of the widely known feature types: Haar-like features and Histogram of Oriented Gradients (HOG) features. The paper also presents a comparative study between the two of the feature types under the consideration of unconcealed gun detection. The classifier was trained with the dataset of 11,257 number of images using both the types of features separately and tested with dataset of 700 number of images. Using the Haar-like features the classifier attained the accuracy of 42.14% with the precision of 45.73%. While using the HOG features, the classifier gained the accuracy of 88.57% with the precision of 95.30%. The evaluation metrics clearly depicts the superiority of HOG features over the Haar-like features in unconcealed gun detection.
机译：由于其广泛的应用，对象检测已成为数字图像处理和计算机视觉领域研究人员的关注焦点。当使用样本训练数据集进行训练时，各种对象分类器可以以显着的准确性和精确度对对象进行检测和分类。任何对象分类算法中的主要步骤都是特征选择。分类器的性能取决于所选特征向量的鲁棒性。本文提出了一种基于Boosted Cascade分类器的隐蔽式枪支检测方法。使用两种众所周知的特征类型训练分类器：类似Haar的特征和定向梯度直方图（HOG）特征。本文还提出了在不隐蔽的枪支检测条件下对两种特征类型进行的比较研究。分别使用两种类型的特征，使用11257个图像的数据集训练分类器，并使用700个图像的数据集进行测试。使用类似Haar的功能，分类器的精度为42.14％，精度为45.73％。在使用HOG功能时，分类器的精度为88.57％，精度为95.30％。评估指标清楚地说明了在无遮掩的枪支检测中，HOG功能优于类似Haar的功能。

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