基于方向梯度直方图的图像中人检测新方法

Narges Ghaedi Bardeh, M. Palhang
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引用次数: 13

摘要

定向梯度直方图(Histograms of Oriented Gradients, HoG)是人体检测中最常用的描述符之一。尽管与该领域的其他描述符相比,它具有良好的性能,但如果图像的大小和数量增加,描述符向量的维数会变得非常大,从而使训练过程的计算变得复杂。为了克服这一问题,本文提出了一种基于特征袋模型的人体检测方法。视觉词是用HoG描述的图片块,然后用K-means算法聚类。为了突出显示最重要的视觉词,可以对描述符向量应用加权方法。在这里,我们使用术语频率-逆文档频率(Tf_Idf),它已用于文档分类。该方法采用支持向量机(SVM)作为二值分类器。我们将我们提出的方法应用于MIT和INRIA数据集,并将我们的算法与文献中类似方法的性能进行了比较。实验结果表明,该方法的性能至少与其他现有方法一样好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New approach for human detection in images using histograms of oriented gradients
Histograms of Oriented Gradients (HoG) is one of the most used descriptors in human detection. Although it has good performance compared to other descriptors in the area, if the size and number of images increase, the dimension of the descriptor vectors would become extremely large and therefore makes the training process computationally complex. To overcome this, in this paper a human detection method based on bag-of-features model is represented. Visual words are patches of pictures described with HoG and then clustered using K-means algorithm. To highlight the most important visual words, a weighting method could be applied to the descriptor vectors. Here we used Term Frequency-Inverse Document Frequency (Tf_Idf) which has been used in document classification. In the proposed approach, Support Vector Machine (SVM) is used as the binary classifier. We applied our proposed method to the MIT and INRIA datasets and compared the performance of our algorithm with a similar method in the literature. The results of our experiments show that our method performs at least as well as other available methods.
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